{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析每层网络的激活情况，做进一步的激活特征分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集验证，需要加载的包库\n",
    "import os\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "import xml\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import json\n",
    "import math\n",
    "from scipy.spatial import distance\n",
    "from collections import OrderedDict\n",
    "import time\n",
    "import pandas as pds\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import models\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from utilour.datasetLoader import MyDataset\n",
    "import torch.optim as optimer\n",
    "from logdir import loginfo\n",
    "from utilour.tool import calaccurary,visualize_cam\n",
    "from torchsummary import summary\n",
    "from utilour.gradcadpp import GradCAM\n",
    "import torchvision\n",
    "from torchvision.utils import make_grid, save_image\n",
    "import copy\n",
    "from utilour.ConvCalAnalys import ModelCalAnalys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "csvpath=\"/media/gis/data/jupyterlabhub/gitcode/hrx/dataset/train_test_vail.csv\" # 文件路径\n",
    "traindataset=MyDataset(csvpath,\"train\")\n",
    "vaildataset=MyDataset(csvpath,\"vail\")\n",
    "testdataset=MyDataset(csvpath,\"test\")\n",
    "trainLoader=DataLoader(dataset=traindataset,batch_size=8,num_workers=8)\n",
    "vailLoader=DataLoader(dataset=vaildataset,batch_size=8,num_workers=8)\n",
    "testLoader=DataLoader(dataset=testdataset,batch_size=8,num_workers=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 109, 109]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 109, 109]             128\n",
      "              ReLU-3         [-1, 64, 109, 109]               0\n",
      "         MaxPool2d-4           [-1, 64, 55, 55]               0\n",
      "            Conv2d-5           [-1, 64, 55, 55]           4,096\n",
      "       BatchNorm2d-6           [-1, 64, 55, 55]             128\n",
      "              ReLU-7           [-1, 64, 55, 55]               0\n",
      "            Conv2d-8           [-1, 64, 55, 55]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 55, 55]             128\n",
      "             ReLU-10           [-1, 64, 55, 55]               0\n",
      "           Conv2d-11          [-1, 256, 55, 55]          16,384\n",
      "      BatchNorm2d-12          [-1, 256, 55, 55]             512\n",
      "           Conv2d-13          [-1, 256, 55, 55]          16,384\n",
      "      BatchNorm2d-14          [-1, 256, 55, 55]             512\n",
      "             ReLU-15          [-1, 256, 55, 55]               0\n",
      "       Bottleneck-16          [-1, 256, 55, 55]               0\n",
      "           Conv2d-17           [-1, 64, 55, 55]          16,384\n",
      "      BatchNorm2d-18           [-1, 64, 55, 55]             128\n",
      "             ReLU-19           [-1, 64, 55, 55]               0\n",
      "           Conv2d-20           [-1, 64, 55, 55]          36,864\n",
      "      BatchNorm2d-21           [-1, 64, 55, 55]             128\n",
      "             ReLU-22           [-1, 64, 55, 55]               0\n",
      "           Conv2d-23          [-1, 256, 55, 55]          16,384\n",
      "      BatchNorm2d-24          [-1, 256, 55, 55]             512\n",
      "             ReLU-25          [-1, 256, 55, 55]               0\n",
      "       Bottleneck-26          [-1, 256, 55, 55]               0\n",
      "           Conv2d-27           [-1, 64, 55, 55]          16,384\n",
      "      BatchNorm2d-28           [-1, 64, 55, 55]             128\n",
      "             ReLU-29           [-1, 64, 55, 55]               0\n",
      "           Conv2d-30           [-1, 64, 55, 55]          36,864\n",
      "      BatchNorm2d-31           [-1, 64, 55, 55]             128\n",
      "             ReLU-32           [-1, 64, 55, 55]               0\n",
      "           Conv2d-33          [-1, 256, 55, 55]          16,384\n",
      "      BatchNorm2d-34          [-1, 256, 55, 55]             512\n",
      "             ReLU-35          [-1, 256, 55, 55]               0\n",
      "       Bottleneck-36          [-1, 256, 55, 55]               0\n",
      "           Conv2d-37          [-1, 128, 55, 55]          32,768\n",
      "      BatchNorm2d-38          [-1, 128, 55, 55]             256\n",
      "             ReLU-39          [-1, 128, 55, 55]               0\n",
      "           Conv2d-40          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-41          [-1, 128, 28, 28]             256\n",
      "             ReLU-42          [-1, 128, 28, 28]               0\n",
      "           Conv2d-43          [-1, 512, 28, 28]          65,536\n",
      "      BatchNorm2d-44          [-1, 512, 28, 28]           1,024\n",
      "           Conv2d-45          [-1, 512, 28, 28]         131,072\n",
      "      BatchNorm2d-46          [-1, 512, 28, 28]           1,024\n",
      "             ReLU-47          [-1, 512, 28, 28]               0\n",
      "       Bottleneck-48          [-1, 512, 28, 28]               0\n",
      "           Conv2d-49          [-1, 128, 28, 28]          65,536\n",
      "      BatchNorm2d-50          [-1, 128, 28, 28]             256\n",
      "             ReLU-51          [-1, 128, 28, 28]               0\n",
      "           Conv2d-52          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-53          [-1, 128, 28, 28]             256\n",
      "             ReLU-54          [-1, 128, 28, 28]               0\n",
      "           Conv2d-55          [-1, 512, 28, 28]          65,536\n",
      "      BatchNorm2d-56          [-1, 512, 28, 28]           1,024\n",
      "             ReLU-57          [-1, 512, 28, 28]               0\n",
      "       Bottleneck-58          [-1, 512, 28, 28]               0\n",
      "           Conv2d-59          [-1, 128, 28, 28]          65,536\n",
      "      BatchNorm2d-60          [-1, 128, 28, 28]             256\n",
      "             ReLU-61          [-1, 128, 28, 28]               0\n",
      "           Conv2d-62          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-63          [-1, 128, 28, 28]             256\n",
      "             ReLU-64          [-1, 128, 28, 28]               0\n",
      "           Conv2d-65          [-1, 512, 28, 28]          65,536\n",
      "      BatchNorm2d-66          [-1, 512, 28, 28]           1,024\n",
      "             ReLU-67          [-1, 512, 28, 28]               0\n",
      "       Bottleneck-68          [-1, 512, 28, 28]               0\n",
      "           Conv2d-69          [-1, 128, 28, 28]          65,536\n",
      "      BatchNorm2d-70          [-1, 128, 28, 28]             256\n",
      "             ReLU-71          [-1, 128, 28, 28]               0\n",
      "           Conv2d-72          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-73          [-1, 128, 28, 28]             256\n",
      "             ReLU-74          [-1, 128, 28, 28]               0\n",
      "           Conv2d-75          [-1, 512, 28, 28]          65,536\n",
      "      BatchNorm2d-76          [-1, 512, 28, 28]           1,024\n",
      "             ReLU-77          [-1, 512, 28, 28]               0\n",
      "       Bottleneck-78          [-1, 512, 28, 28]               0\n",
      "           Conv2d-79          [-1, 256, 28, 28]         131,072\n",
      "      BatchNorm2d-80          [-1, 256, 28, 28]             512\n",
      "             ReLU-81          [-1, 256, 28, 28]               0\n",
      "           Conv2d-82          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-83          [-1, 256, 14, 14]             512\n",
      "             ReLU-84          [-1, 256, 14, 14]               0\n",
      "           Conv2d-85         [-1, 1024, 14, 14]         262,144\n",
      "      BatchNorm2d-86         [-1, 1024, 14, 14]           2,048\n",
      "           Conv2d-87         [-1, 1024, 14, 14]         524,288\n",
      "      BatchNorm2d-88         [-1, 1024, 14, 14]           2,048\n",
      "             ReLU-89         [-1, 1024, 14, 14]               0\n",
      "       Bottleneck-90         [-1, 1024, 14, 14]               0\n",
      "           Conv2d-91          [-1, 256, 14, 14]         262,144\n",
      "      BatchNorm2d-92          [-1, 256, 14, 14]             512\n",
      "             ReLU-93          [-1, 256, 14, 14]               0\n",
      "           Conv2d-94          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-95          [-1, 256, 14, 14]             512\n",
      "             ReLU-96          [-1, 256, 14, 14]               0\n",
      "           Conv2d-97         [-1, 1024, 14, 14]         262,144\n",
      "      BatchNorm2d-98         [-1, 1024, 14, 14]           2,048\n",
      "             ReLU-99         [-1, 1024, 14, 14]               0\n",
      "      Bottleneck-100         [-1, 1024, 14, 14]               0\n",
      "          Conv2d-101          [-1, 256, 14, 14]         262,144\n",
      "     BatchNorm2d-102          [-1, 256, 14, 14]             512\n",
      "            ReLU-103          [-1, 256, 14, 14]               0\n",
      "          Conv2d-104          [-1, 256, 14, 14]         589,824\n",
      "     BatchNorm2d-105          [-1, 256, 14, 14]             512\n",
      "            ReLU-106          [-1, 256, 14, 14]               0\n",
      "          Conv2d-107         [-1, 1024, 14, 14]         262,144\n",
      "     BatchNorm2d-108         [-1, 1024, 14, 14]           2,048\n",
      "            ReLU-109         [-1, 1024, 14, 14]               0\n",
      "      Bottleneck-110         [-1, 1024, 14, 14]               0\n",
      "          Conv2d-111          [-1, 256, 14, 14]         262,144\n",
      "     BatchNorm2d-112          [-1, 256, 14, 14]             512\n",
      "            ReLU-113          [-1, 256, 14, 14]               0\n",
      "          Conv2d-114          [-1, 256, 14, 14]         589,824\n",
      "     BatchNorm2d-115          [-1, 256, 14, 14]             512\n",
      "            ReLU-116          [-1, 256, 14, 14]               0\n",
      "          Conv2d-117         [-1, 1024, 14, 14]         262,144\n",
      "     BatchNorm2d-118         [-1, 1024, 14, 14]           2,048\n",
      "            ReLU-119         [-1, 1024, 14, 14]               0\n",
      "      Bottleneck-120         [-1, 1024, 14, 14]               0\n",
      "          Conv2d-121          [-1, 256, 14, 14]         262,144\n",
      "     BatchNorm2d-122          [-1, 256, 14, 14]             512\n",
      "            ReLU-123          [-1, 256, 14, 14]               0\n",
      "          Conv2d-124          [-1, 256, 14, 14]         589,824\n",
      "     BatchNorm2d-125          [-1, 256, 14, 14]             512\n",
      "            ReLU-126          [-1, 256, 14, 14]               0\n",
      "          Conv2d-127         [-1, 1024, 14, 14]         262,144\n",
      "     BatchNorm2d-128         [-1, 1024, 14, 14]           2,048\n",
      "            ReLU-129         [-1, 1024, 14, 14]               0\n",
      "      Bottleneck-130         [-1, 1024, 14, 14]               0\n",
      "          Conv2d-131          [-1, 256, 14, 14]         262,144\n",
      "     BatchNorm2d-132          [-1, 256, 14, 14]             512\n",
      "            ReLU-133          [-1, 256, 14, 14]               0\n",
      "          Conv2d-134          [-1, 256, 14, 14]         589,824\n",
      "     BatchNorm2d-135          [-1, 256, 14, 14]             512\n",
      "            ReLU-136          [-1, 256, 14, 14]               0\n",
      "          Conv2d-137         [-1, 1024, 14, 14]         262,144\n",
      "     BatchNorm2d-138         [-1, 1024, 14, 14]           2,048\n",
      "            ReLU-139         [-1, 1024, 14, 14]               0\n",
      "      Bottleneck-140         [-1, 1024, 14, 14]               0\n",
      "          Conv2d-141          [-1, 512, 14, 14]         524,288\n",
      "     BatchNorm2d-142          [-1, 512, 14, 14]           1,024\n",
      "            ReLU-143          [-1, 512, 14, 14]               0\n",
      "          Conv2d-144            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-145            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-146            [-1, 512, 7, 7]               0\n",
      "          Conv2d-147           [-1, 2048, 7, 7]       1,048,576\n",
      "     BatchNorm2d-148           [-1, 2048, 7, 7]           4,096\n",
      "          Conv2d-149           [-1, 2048, 7, 7]       2,097,152\n",
      "     BatchNorm2d-150           [-1, 2048, 7, 7]           4,096\n",
      "            ReLU-151           [-1, 2048, 7, 7]               0\n",
      "      Bottleneck-152           [-1, 2048, 7, 7]               0\n",
      "          Conv2d-153            [-1, 512, 7, 7]       1,048,576\n",
      "     BatchNorm2d-154            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-155            [-1, 512, 7, 7]               0\n",
      "          Conv2d-156            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-157            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-158            [-1, 512, 7, 7]               0\n",
      "          Conv2d-159           [-1, 2048, 7, 7]       1,048,576\n",
      "     BatchNorm2d-160           [-1, 2048, 7, 7]           4,096\n",
      "            ReLU-161           [-1, 2048, 7, 7]               0\n",
      "      Bottleneck-162           [-1, 2048, 7, 7]               0\n",
      "          Conv2d-163            [-1, 512, 7, 7]       1,048,576\n",
      "     BatchNorm2d-164            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-165            [-1, 512, 7, 7]               0\n",
      "          Conv2d-166            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-167            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-168            [-1, 512, 7, 7]               0\n",
      "          Conv2d-169           [-1, 2048, 7, 7]       1,048,576\n",
      "     BatchNorm2d-170           [-1, 2048, 7, 7]           4,096\n",
      "            ReLU-171           [-1, 2048, 7, 7]               0\n",
      "      Bottleneck-172           [-1, 2048, 7, 7]               0\n",
      "AdaptiveAvgPool2d-173           [-1, 2048, 1, 1]               0\n",
      "          Linear-174                    [-1, 3]           6,147\n",
      "================================================================\n",
      "Total params: 23,514,179\n",
      "Trainable params: 23,514,179\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.54\n",
      "Forward/backward pass size (MB): 281.19\n",
      "Params size (MB): 89.70\n",
      "Estimated Total Size (MB): 371.43\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 加载模型--修改模型\n",
    "resnet50cpk=torch.load(os.path.join(\".\",\"modelRecord\",\"resnet50.pkl\"))\n",
    "summary(resnet50cpk,input_size=(3,217,217))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool', 'fc'])\n"
     ]
    }
   ],
   "source": [
    "print(resnet50cpk._modules.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"
     ]
    }
   ],
   "source": [
    "print(resnet50cpk._modules[\"layer1\"][0].conv1) # 这样获取所有的层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "calmode=ModelCalAnalys(resnet50cpk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "for step,(data,label,imgpath) in  enumerate(testLoader):\n",
    "    \"\"\"测试波段运行\"\"\"\n",
    "    data=data.cuda()\n",
    "    label=label.cuda()\n",
    "    tlabels=calmode.forward(data)\n",
    "    tlabels.detach().cpu().numpy()\n",
    "    print(label.size())\n",
    "    #imglist=calmode.saveImage(label,tlabels)\n",
    "    break\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "self.activateinfo.append({\"type\":0,\"name\":class_name,\"input\":input_act,\"output\":output})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "\tConv2d_0\t(8, 3, 214, 214)\t(8, 64, 107, 107)\n",
      "1\n",
      "\tBatchNorm2d_1\t(8, 64, 107, 107)\t(8, 64, 107, 107)\n",
      "1\n",
      "\tReLU_2\t(8, 64, 107, 107)\t(8, 64, 107, 107)\n",
      "1\n",
      "\tMaxPool2d_3\t(8, 64, 107, 107)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_4\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_5\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_6\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_7\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_8\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_9\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_10\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_11\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tConv2d_12\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_13\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "Sequential_14\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tReLU_15\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBottleneck_16\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tConv2d_17\t(8, 256, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_18\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_19\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_20\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_21\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_22\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_23\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_24\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tReLU_25\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBottleneck_26\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tConv2d_27\t(8, 256, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_28\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_29\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_30\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_31\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tReLU_32\t(8, 64, 54, 54)\t(8, 64, 54, 54)\n",
      "1\n",
      "\tConv2d_33\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_34\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tReLU_35\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tBottleneck_36\t(8, 256, 54, 54)\t(8, 256, 54, 54)\n",
      "Sequential_37\t(8, 64, 54, 54)\t(8, 256, 54, 54)\n",
      "1\n",
      "\tConv2d_38\t(8, 256, 54, 54)\t(8, 128, 54, 54)\n",
      "1\n",
      "\tBatchNorm2d_39\t(8, 128, 54, 54)\t(8, 128, 54, 54)\n",
      "1\n",
      "\tReLU_40\t(8, 128, 54, 54)\t(8, 128, 54, 54)\n",
      "1\n",
      "\tConv2d_41\t(8, 128, 54, 54)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_42\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_43\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_44\t(8, 128, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_45\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tConv2d_46\t(8, 256, 54, 54)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_47\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "Sequential_48\t(8, 256, 54, 54)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tReLU_49\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBottleneck_50\t(8, 256, 54, 54)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tConv2d_51\t(8, 512, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_52\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_53\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_54\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_55\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_56\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_57\t(8, 128, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_58\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tReLU_59\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBottleneck_60\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tConv2d_61\t(8, 512, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_62\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_63\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_64\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_65\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_66\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_67\t(8, 128, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_68\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tReLU_69\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBottleneck_70\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tConv2d_71\t(8, 512, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_72\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_73\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_74\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_75\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tReLU_76\t(8, 128, 27, 27)\t(8, 128, 27, 27)\n",
      "1\n",
      "\tConv2d_77\t(8, 128, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_78\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tReLU_79\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tBottleneck_80\t(8, 512, 27, 27)\t(8, 512, 27, 27)\n",
      "Sequential_81\t(8, 256, 54, 54)\t(8, 512, 27, 27)\n",
      "1\n",
      "\tConv2d_82\t(8, 512, 27, 27)\t(8, 256, 27, 27)\n",
      "1\n",
      "\tBatchNorm2d_83\t(8, 256, 27, 27)\t(8, 256, 27, 27)\n",
      "1\n",
      "\tReLU_84\t(8, 256, 27, 27)\t(8, 256, 27, 27)\n",
      "1\n",
      "\tConv2d_85\t(8, 256, 27, 27)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_86\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_87\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_88\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_89\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_90\t(8, 512, 27, 27)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_91\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "Sequential_92\t(8, 512, 27, 27)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_93\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_94\t(8, 512, 27, 27)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_95\t(8, 1024, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_96\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_97\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_98\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_99\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_100\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_101\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_102\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_103\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_104\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_105\t(8, 1024, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_106\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_107\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_108\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_109\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_110\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_111\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_112\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_113\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_114\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_115\t(8, 1024, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_116\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_117\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_118\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_119\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_120\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_121\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_122\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_123\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_124\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_125\t(8, 1024, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_126\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_127\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_128\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_129\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_130\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_131\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_132\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_133\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_134\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_135\t(8, 1024, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_136\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_137\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_138\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_139\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tReLU_140\t(8, 256, 14, 14)\t(8, 256, 14, 14)\n",
      "1\n",
      "\tConv2d_141\t(8, 256, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_142\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tReLU_143\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tBottleneck_144\t(8, 1024, 14, 14)\t(8, 1024, 14, 14)\n",
      "Sequential_145\t(8, 512, 27, 27)\t(8, 1024, 14, 14)\n",
      "1\n",
      "\tConv2d_146\t(8, 1024, 14, 14)\t(8, 512, 14, 14)\n",
      "1\n",
      "\tBatchNorm2d_147\t(8, 512, 14, 14)\t(8, 512, 14, 14)\n",
      "1\n",
      "\tReLU_148\t(8, 512, 14, 14)\t(8, 512, 14, 14)\n",
      "1\n",
      "\tConv2d_149\t(8, 512, 14, 14)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_150\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tReLU_151\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tConv2d_152\t(8, 512, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_153\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tConv2d_154\t(8, 1024, 14, 14)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_155\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "Sequential_156\t(8, 1024, 14, 14)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tReLU_157\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBottleneck_158\t(8, 1024, 14, 14)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tConv2d_159\t(8, 2048, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_160\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tReLU_161\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tConv2d_162\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_163\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tReLU_164\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tConv2d_165\t(8, 512, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_166\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tReLU_167\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBottleneck_168\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tConv2d_169\t(8, 2048, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_170\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tReLU_171\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tConv2d_172\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_173\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tReLU_174\t(8, 512, 7, 7)\t(8, 512, 7, 7)\n",
      "1\n",
      "\tConv2d_175\t(8, 512, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBatchNorm2d_176\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tReLU_177\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tBottleneck_178\t(8, 2048, 7, 7)\t(8, 2048, 7, 7)\n",
      "Sequential_179\t(8, 1024, 14, 14)\t(8, 2048, 7, 7)\n",
      "1\n",
      "\tAdaptiveAvgPool2d_180\t(8, 2048, 7, 7)\t(8, 2048, 1, 1)\n",
      "1\n",
      "\tLinear_181\t(8, 2048)\t(8, 3)\n",
      "1\n",
      "\tResNet_182\t(8, 3, 214, 214)\t(8, 3)\n"
     ]
    }
   ],
   "source": [
    "for temp in calmode.activateinfo:\n",
    "    if temp[\"type\"]==0:\n",
    "        print(len(temp[\"input\"]))\n",
    "        print(\"\\t{}\\t{}\\t{}\".format(temp[\"name\"],str(temp[\"input\"][0].shape),str(temp[\"output\"].shape)))\n",
    "    else:\n",
    "        print(\"{}\\t{}\\t{}\".format(temp[\"name\"],str(temp[\"input\"][0].shape),str(temp[\"output\"].shape)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(calmode.activateinfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for temp in calmode.activateinfo:\n",
    "    print(len(temp[\"input\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f56d895f590>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nRGAx5wUREGfI3zQRlz7n07yLqMpMFLTHThwh/XWi5XN+3Hze8to1yOOJWGCLS9csx1fWiUDjYjlXgRgd46hobIxrEcEL0eWsO5ny9esJJzm82AgWiUCxSWuWOXfWnmTKIlxTCU7MLFFP3IlSWezHc2U8U+IuIrsR30aci4aEg4ejM7vE4BhFOfqG823Pxhtm+yZaYNNKtqmLlmTkBzVVAduH48aBOIZnnn7T4pwiSbJQpRCAtwlPgwhki0jOInSaxPm0uLAQ0aHo0MkLkINP7FpNeesT0suJiJ4/2PGmCfPjsKq/yQ3vz/uId5GuCTQ+4CpE9G5JQapnSRwBJqQFZhtl7fcl1Oc7Ubv/QhLIEkms7lnDrh0YoyNER1BhCJ6jaxCB0Suh9+goaHLoB08R5bNLbrLwa7KPUNxkbiARB5uXG2VOi7O9JtN4nX+ui63MXIkyERH1Jv4PL5TxeYRzQ/68FmH0xINHrj3+2uHGRDCAI7DtjHOrBpyPxUSiyUqd6w+YDULTZ2NYDIBGmiuheyVo03JQQV5o2l/CWNkF8p46ldQeRiSeCBFwoMEwLCN2FueLXzd/TwjvzLCDYNTWpayvJFsukTWLyvUC55chojR+jqiu+i1fF1VuRMRNO9K4yKYZaVPcZz63cRGH4iSeIHpUR1xR9teIwF3Qpfs2LtxIUOr7LmHbGBE4hJYheMbG0frAwQeG1nOQjtg64hjR0UFYiLLFaGj2F5rEvkeH9A5/5XDHtK5Z1M+XaiVI1NrEQrPIXop8Xk4401SjACBsYXwR8M8H2m5EFYa+IfbG/d3e0VxZkpAbzWgInlHheNYwRruBpjnWZiVJKsCyYEo2r7hB6a6U8BmA4yANw7ahaUKJNbjBVDOtxT2kvhqeBhHIsrZ3Jn4l6239EwLSRFwbjev6ONN986Z3YqJ4Ls6SVXkn9lkrHVwSt3QCjZ8HbBcikKcItxKBbTOy8SOdCydI6ETZ+YFmJQokqhARwgmiuvJ7OXZHIMC576c1kIjP3G9l7DXCktWFs6anj4YM177Dy4Y+2N1DdITgUiWgSbmXRIyBIjF5HwsCDvuWeBT83s0NgUzcf+mtWMZbZEu/JpUvRyTGNhn92kR0WqXdDWx3th7HY0s8GvdvLh3+ILQX0F4aQo87cwmqdzbXlAoYg0dGOcVYnaQBm5ckQ6P95o+R7tKhXgg7oe89nM2lQlVBXFxVzb5tkoCIfC/w7wLfhb2OP6mq/xcR+beB3wl8K536B1KVoXcPzoiAbCKuM39RJgLiTaRq20DXjHinZdO6gsyKTwtdjDEoEeE4NkUfrhHAp+s2zWRWdsylgAw18cjf8+cX3YHOBXZ+wC2Q3YvSuRG/Eg8acCfifv5ec+s1QrG8x871RRVY3ivg8EQCE3G56b4ZjrHlKnR0LnAxbPAuEqJjCG62Fk4oa16vrxOlHxsuo2OovCzF2wPzAiA6BdZNfngqYqAMz1PuQZEWFekizWak7cY0J8oe6UdPCA6ODn/taC4FfzQC0F1qMfaNR3OBjqNjCJkICL6aa4kLyJ6FnFXosYln4pDChX2vuN5sDiE4uibgnZqNWhSyfaBiOA+TAQweIwmMwO9V1b8jIs+Bvy0ifz399sdV9Y88Yuw3Au3MWd9sRpo2MPQNvjNDm/eRs03PNiGrE6X1gdYFGhfp3HS8qcTuzGkPY0tMOlm9SfO/s6Yv8zDRPUsWtgPG6IlIIRBAGQ/gw80VjQRaCWVMAI/NpZWAX5EEQlIHwluoDZbv4VDatB4+zTcgeJRB/eye+fz8PGfO1uE6dlyELU4ijUSetUde9TtTE3RClMzdMkHOFu/82btYjKRA0dtn3L0y5s5CjLN04FJEXhdxzweaZpLRvVe6dqRLNp0QBe+UrhktOE9Bg/nw/cHckf7IVJo8uRZzyfM4eK76Fu/Mg+B7LJBJ5/PTFL6sIlTRHkYkGitV5nrBH8BdeYZnDY2PRRoFIwS+stMUw/DCFnUXvDERUNVvAN9Iny9E5O9jTUd+/sC7kkapKsQotF3kvfM9L7oju2ZgVMfVYN1JnrcHvmt3wQftFVs3AJwg06C+IH3mujXHa1aMdhmxAWo1Y6kO1GPt/FAQavY3IX6bWI6vNkzAdPP8d6jSAOvrfbIn3Lp0KK2MRQpoZTSpqJIIWglGBLB79TrfPlGFM3ck4mjDDofy3B8AOMSWj/wLrsaOQ2jMbhD9bK1C5frKxHYYPUPfwNEjQQibSSIoU8uJRUm0105NJYRiX5BtoO1GttuBTROICmMiRMWvP3raJswkwj54VAUXLPzXVZw9tkniaqfwX8jjhhL/X3oizAyT1cIt7CIqggvQHJX2CppLoX/R0m3Gog4spcysujkWEtkNbt4a3opNQES+H/jVwP8H+HXA7xGR3wr8LUxa+GzlmlnzkUdBXeQ/QhiTYSYFU3z57IoffPYxAN88POey3yTddeCf2X2TX9J9XIYKTKJ0wBUReuuGIl5nhIrqcBLLuRkxjrEt42SkbVMUysRt50h50G5GJNZEf2AmDYR036jCQVuczufVJsmilTAT5evx6/HO3LHMOc+3/n0rQyGSEUevnohL8zBC1EogJCkhjxFwXIYtsRN2fsPFuMUNyuWwIYoUqaC8TigSQVCxqLneXL2FCCgpQ48i7murxLOAbIMhjJ9cq20TiueldZGgwgFzuakKIRrhOfc9SrKDJALlmpg8FTIlEuU6hgKxMde0NjaxEJNslKoR19mIOZ4henApfFFdVbW4WgQ3Ks1eaK6EYe8Znzk2rbIsJhwrKmLRhXfkhC/g0URARJ4Bfwn436rqaxH5E8CP2mPwo8AfBX778rq32nwkg3OmFyYiQLQNcNb0fNheArAPLRt/zjE0bNzIh81rvty85iJuZ1wWKIjqJRbRt+aqmQuDIU6vnoO2HLQtx4yjxoI8nQQckS5x9nz9QdsZgsFcKqm5fIYgRqSiOIgUcX1QxyHFMbcSaN2IR4tIDxMhq7/na5woXRU6l+cRcHQylvnX0sVENO3e0xrENCdf1JpGTP/vfDCJzQkhuskVGh347HJV+m7kuG0IjlTJR+eeBLBjjdI969lsBs66AZ/iFkJ0pua5OHvDToBKknOiRnQSYWh8wLvIZjNwvesIG6HZmwfK/P/53pW1/+g57E3SlINPiK9FCjAVwiYfXLqYyT1aGwwhEYKDIEeTbH12j1bqqs+qURWjkaEce1eSgIi0GAH491T1/2k304+q3/8U8Fcfc497g/NokwL8h6S3JwOKK9zYDG+bZkSSxX3rhrRRG7wMhXsBBeE9kQPthNQJoRAICTnzdQMerxP334qJ+Yb6bjZ+n67NSF8Ti5q7wpxIAIWrm7riOGjDddhwjA3XsWMfWsZkpW6SjpQlBLtXFh9rL0QyhlZqQG0IvBo3NMl4eeZ6Nm6c2Soyocp2gzPXc+aPRiDUcRk27EPLMU4qliTkrNWqOvah84Hz3ZEYhTB6Gh9xybuTz83juKTLb9uxEJghOjSpYhnBQ+LWGbKhzbtIP/pqTEfnA5t25HobiJupXkVdpEKiJQb5vRBbR9AWieZC9MeUjhwo3L52GaqTyc6h07m51qC6bDC0C2rvwPTepiCxO+q+rMJjvAMC/Gng76vqH6uOf3eyFwD8JuDH3vQeDwKNENSMMHuPdhGSO3DnBzZJ5wdoJICHjRvoJOBRzuTIuTuyzcisfsbltzLMuag6slMwqGOg4aAtg+a/Hoe52VoZC6ekXCNFBTCRPnNbKYhdG+COsZldn3XnfO4+mCX+MLb00Z/o267a+LU+uXRZLr/XxrrD2NC4SOsDnQu0PiQjqm3MrR9n1+/8wHljKsY+tHzWnzFGx6iePtgcl0FK5XWmv16UbTuiZ0dUhW034BPhqOdXX+fTs2YpIKQAm+zeDUn8r4/FCsF8cVWaya5rAr6LhI1aByJnHFoRcnsxN0CzB3XOSo+N0F4KzbUmQpCs/hWWWgahEv1UIk29OQpiI1MMQ1VEJSN8nnteA3uGh7sH4XGSwK8D/jXg/yci/3U69geA3yIiP4y9j58Gftcj7nF/UEX6geZKaS+F8cwRu8jz7ZGvbl7z3B34PJyZ/7riWN8cX5xwf4ArPNdxMyMEWdct+vDCbpAJwHXsOMa2cEQvkevQzQyMY/QcY2OW8mgWc6AyihkByNcMwc/8/HmDZyQ4jo1xMbKeO3dBilAST2y5lr7l+fn5HFXQHPySjgtTUFUeG+D959cFObNhr01ifT82s8jHmhPne7ZJX89W75CRU5RdZ0S8dbF4E8ZY53pP61JbyjPC1B6IkGxFGZHGJP5rmkOO+RiDL9c7HwhtJG4c2gO9IbQ6s+7LqPh9Er33iQhcKc3eXH25F4FWcQvoZM7KC2uc/7SSUkmNX4ArBG/9vd4HHuMd+M+Zh2Zk+LZ3Hcog/UB7rTRXzgw1beS7z1/zfnvFoLWYbBv04+Mzfozv4WeaL/HcH3i/ueKlvwLgIuy4iFuOSU++jh1DNN12HzoGdSdBMxFhiJ4+ceFRJ258PXQTV4XiL8+x4DnoY9Lhpu8WM14tdTmekTQrhTctzOJ7pUuXLERdnKvVeUkUxev0eXkvgW8eGlxryBaCQ6PgUmq0RquuU0dkej/lSQhwGBo2TWDXGsLXrtjWRVvTtF41kudzYPLMFMdBZR0P6bpRTwlQfvQheNoUwp2v8y7StoFhExmeRWRwlhZdZaa6waL9JJh478YkAQx1UBDFiJnDhl3SKQzRpUgK+Rop78Hecy3VLOMy1o7dB55GxGCGY093GXC9GcrazcgvPnvFc3fgIm75pH/GZ8czrofOwlvHlo/2zy0irxl42V3zojniJLIPXXFnjeq5SkjcBz8LeFkm8Sw5cf49jBO3oUbeuJIFViNZbfy66Vjyhc9SkNf2gujpdfWmaeP8+PKcXKwiggxuKtGVuduhIzo1ETblYIScHi2a1sA4mjSWK+9Ecd4Sp0JwjKOnH33Kn5iQ1IueBGplqCPpJFv0Z3qynHDIHKQUc0yHKI0P9GNDiM7sFEki8KKcbQb6XcvYO8LeCoxatyGKzu4CuGBp7C4kFaB28lQ2zIzcpVtxSDkRyXsQUtahS3aCfGHMLgmo9t58r2Wb6X3hSREB7XvayxE3tgiw3Qy831zz0l9zHTd83J/z0fUzro+diVBRCod1LkUUps03Bkc/eobBz8XhJfeta7/VuunMRLuC1BnB8u+1Ll5v2DUOXf+tz1vWoVtCkRhWKIQofhvQmDZXLXlkbjqmBK1BkCNWJae6v7qUGSiWgadnhkQarcKQ+FjGtXwaIaQ6AzAiQiIEjqbxlk/hI2PwBJknNc0eS6W48/zid0nvJV83Bod3OvNE+KyCOLM/SAok24hyTGrMrh243gyMvgWX8gUUmsTpNYUG+L7i5AnZS7p0mRRTqDOTwbDQZwe0Yl2PR3CDSRfeKyFKCWEHKsnx1FA4u+VNe4InRgQyWB13ZdOObN3AQVs+Hc95ddxxedhw2HdWtimJrERsc9apw2GBCBnyYta6nagVs1hD3vw5Fx1RSbJ+db5q9TtzRF8SgeXYJ/Nb/F2THoBc+WdKrALnouXYL26Qr4rexHodvW1kEscbrLJPNM8YYadoG5E2GuILliqdDXGke0WHYqHdYfQ4HwtxzeJ7ri3gV6o51ctxW17G7FlWzvGVBNU1Y7FXNBKhMWPnECx8OEtAkohe7jBkWY5acXggcfma6wNopdzP2pS5bCA0AuAmU1VaE0qMQCYEtYHwTeFpEgEPcas83xw5cz0XYcdHxxe8OuzY7zvCvpmQPCOD0xK/beJd5qqgotM5SKknVza4qOW8s5QAFvPSJKgVhNR1VaCcw6n+vUTmDBmZVxekuiY/W6rqI3XKdeKYksYrfS+LJODJvu7SFVjNCOaPxtGMADN1AkrErdCmPH7FqYwwGIEprsNoEsJYro2Ace083yW3Wz5yhlo1qBG+GP0qg2HrKXECJg0YJu6HlhgccpQpKSivT7S6AL7XqQbCEvm10vPr2aXfVcweENtEJEeTDqZAJHOB5tgH1blEkJ/nTeBJEoHYgG4D72+uaWXko+E9Pjo85/KwIRw95AquCdQrNLFiedahBijSgRl1TL+VlIvgimFLZ3nds0o/aZhYJc2U91YbFcNEgOyampWv6OdLCaGWSlbGn0EiBFa0UyzsVtWq4Top2ZFLrll0TbEknChGcN0AejSfuPm6BcTRe496q5akNcKqcf9SMDOlcVunnrRGYpGCdQ0GkQDJMFY/WW0ovIko1FJC7RrMiUt98PRjQz82jKNHRHm52fOi2wOwH1uTVEaZpQK7oCWhKSP9ybJnArAyuUIgVIthUFJTlNgI/XOhfw66C7SN5bmMsYo3faQUAE+NCIiYOOvBbQPvtRa3/slwzif7M46H1iQAAXycI1CF4EBCxqy/pt8AcRPi15btmqtRfa6KHq1C/n0Ui0PVnFmWJACtpBOTXNIuq8eTpMac9Dao2FVNNdKEJZXqVodV/Bmdie1NzbmnOTofURzSKNbGS9FGGEdvfvIr8EdzhVlknadvFX8+zIhKTMiuo7O4/lylt5J6YlIFVF1a59MnK2Oplfi6rzrgRAsSFXdjcBwPLcNlZ6HnQfhgd82XJNK4wHnTs9sMXGWEz39HIwRFQqr31Bqhrjj/ydxUIQhOldAJ/bmw/1A4fhhozwd23UDrg3k58jqIEHmcSvC0iAAY02yg24ycN0cGbfi0PzcpYHRF95c2TgifdVYX8V5nNf2WkN1clv8+ee6zQao2BuuM+9wxbSEVjgSyq0el1CaMwyJsuMZvIFdEqsez+efdySRpAJL6FroIDBCd5cNrE4sUNK9nKDSNFb0cE9KBEam4E4bg6D4XOEJzMNE4to6w8YQm4rtYuQenyZfGoXBjrALkKkvTsVj9HhNS1Gu/JAhLV2A2MuZU8DE6hkND+3GDBBhGx89u3iOoY9cMXCWPkoyG+FkSKOHCQtHn1zZOLmRa5lDyXZhJCOYiFGIr9O8Jxw8j8n7P+dmRbWP1JnrxFE/BLc98X3haREBc6u1mRsGNGzlow+thSz80Vhiyps6ZMsvks57CUec6cQinCncWcWXlhaxJBrOpLl6YFRdNXC0ackawmncKkriwyYkVBcjl0KoqxblScLlzQi7NHTFwyR2VKjGTbAOtlN/UOTSFGy8lGmvPbQecj8SdCViHDx3Nzm7cXSibT5XQOQ60hPNI2ERcG4r9pCZcuVb+jACkxiNaGf2img0nD3GTHlwfPyEqMq+hCDCOHo7e6gX0IMFz6Z7xUxdbSyCKQtw37HrL8Kv3UXHu1AQgr9fSHlAbAtN5Ul+S1IBxKwzPILw3cn7es2nH4sWYCYGV9+NNpYGnRQSclPzy1lv2XFRHH7y5AmcBN2njFW7riaJW8QbKm3Wp3uCcS8mJ0awEwlDbBqbbLSWBtWowMU7XLX93jUITzHYQq3qFMpVJq+siSjW3EnCU75UqJwMQkz1xFKv/pySJQKcApeyPTvEP5bmzBLUJRIHjV5Txylno7AjttbL7Fog6+hfC+MwRdwJdnK1fHc14SsCMcFlgUEWQ3pDrnRDf9D0Ehxxt3v6QkbaxfIE0n2Y0+0ctcmQPgQSF3BU7Ty/9m9kD1ghARWFVUoPSBuImNSNpB7pF5ao894BJSKFSox4qETwNIuA8xJQk0znCRtm1I8/8kaBuQl6hbKoZqBCDIdoUoKIzZC6BGQu3ocg8NLcu1V1E3QWlnjKfp7FqSUOE0gNg2VfAufm1+UlywEv9TPN5alqmSRqyAhkJ+YIStpge7gQ2ibHlOIoqYcd5k1rqwBXXBeIohLPI4cugznH+ddi+itbB9+joB2EIEJ5jLkTJdoC8g81F5vxU2DM/Uw6Rtjlkv34s4nyGOjBomZcgomx8KJGHwJRpOHr8IRk3AddD9xqrGZCWVp0RiLx2btQ5chdukac+sfjyecbGF++31EWQqf8FhuQ5/HqIfubtyO/WO4hqsmJMWZN1gZF3Xk/g5xvEe1RjkhUnXKiLcsRFn7oiwy2oZm2Zn1vpTxESSD712n6gzN90vvZmAnDTC1r2FZhx9tmJUu46G3d5To6HqDdQ4lJuMNdXnbEskgJYZsTwhjkrSGfWc43CeK70LwSfxOfN5ya6qBeLv28UJTUoraImRZOdIbvaVIhiRMelWIb6/g/lenGxlmUsyW3FrRKQG5Lun3T5bLE/sfBntTIt05sJ5NVwycsQGyFuIu1mZNcOq1GPmevXeRhvAk+CCGgIJop1LccXnvA88HK7LwUuRnUW9XeXGJmNcZWfZxY4s/IiJJ0TUxNO49SLDbZ2q1so81K/OzWUrYy39ttNhCdLRelCZ71bcL1M3ZsW61SrBsvw58L5vPnylcD4Ag7qwDk2n5mhcPtpusY5BsV6PVQ9/ArXmkUrmnvV1iHM1Jz084MgqJzYA0jfg1diJxPSJ86MMKtktCp0VcTgVqhsCbf9HjYQziPvnx3ZNKMZLnMWKNP7fkwKcYYnQQSyKsCm4/Alx+6DS77v/DNaCXw+PrMiobPqtqwSgyL2V5y14MpC/J4ukiJVaBJvnbsBge4Ja/aC2ffl/eu/TEh/cnK9AZMgZDHudo3vQVvM0JgJUUL6WspYIwCqkmwTijSg5yN944mNJzZC9zlsXiubV6Zy+INneJ46+2wD0iwxixmiaAoeik5TGHdExOH9VJn5tkKqGXJWoq9EalOVImNnzUYlNTcJnUU/SgC/l5QkVC99JW7flwgs30X12JqeOTdAlZ1JAV6ipZKnGInTeowPuO8KPA0iAEnUdAzn8Gx35GVzTUA4xNbqxNXqwEIvq2Emtsu0Fydpf4GQlYguiRiE5Ut5iNV2wWLWRP9prvXx9eeY/VYRQS2iPiUJxg1JSXXp+lglRlX6f90vcPU+YN6Ks5HRKUdtUJeKbh6V3cdKcy0cj8JRHYNXaOaGL3HZaJgVcjOc5gadMI/+uy+EOBGOWZZiGzjugqkqSQIIW2sX7veph+KIrU1yCZYnrwSse8G6xljGio0ZB8VPodahmmu2j6zVEHhozwF4UkTAIWMoYZvHOFXaGYOzwJS6132G+oVojey22cWl+PoaASpiknvgzIjGbZx7unT6XIv/a9eq3IjwtyH7jT3q1OaYLdjlb9Z7xZQgjfOIvbqV2up9HVY/P4dkO4U2Mp5Z+u2xN4mgS+W6zbcuqPOMgmUxShojBWPlevxRxZqa5i69jcVoNBVC3zdsdpmI1LrArhs4ng2Mz80ook1EdtbwI8bO5pW7HmfNsrhbpjW8N8w1oHKsdCuu6gfkuhJRBc/tRUS+rfUEfqGBmFnfAoW8hVceteFq3FghyyoXAKi44ilRKJFrJgMjteFv+dJm12FvrlY3FghZEGrJ8fO5J5LAYl4nc1hugpt/W2VVlZaUTSGSYghidpcWN+lSbFreK+VQSCwxGSKgnRLOlGP2PGABRc1e2XxGOuYZnwGpIaw1I5gIpOYOx1lkVmsBHqIracbLQKM1qMu5g43TuMjzzZHjruHqpRGZbjNwthk4jp6Lg7fOSW1VOLRiHkti+mA42RyU/VRLYiHaOi0JwNJw/FB4bI3BnwYusHSUUVX/2yLyAfAXgO/HKgv9q2vVht8FaNswnisf7K55z++5jh2fD1sLBHlTvWmB6LBY9CwBVBvQSsyfis7ZJZTTaIs+CQXhT67Lc8iEKZ1bflp7rjUuXeOvVv/Sb8u2BapMktPKGqzeTykxC4ClEPuIbpIfW0HUJTekxRF0l5Y0Ezshdo64iQv9K0kkOcw4r6NYAFeI5tG4rxSwLK2WCUKWBnI1pGebnrO257LfcLXZEjbtVEJMamJevcobRa/7Q52DsJy3EYL58cdmEMLbkQT+B6r6cfX99wP/iar+YRH5/en773sL97kZRNBxhMYznkVedte0bmQIO/7JxUsOn20taaidZDDXRktiCSmxe8np8kvWzNG0ut2KSlGdWx1aP295k2rMk0ChPGVXzVEmrFy05CzPdxNYFB4WDJRbcsk0leZaGLoWfT7guxR7EQwBLWpRZoVQSoBSmltMKoUIpg7oZB8Izqf7uROJoHtlcnbohLgxS71sR7MD9H6K9kxBXzEIvUwuwxxS3DVjsaK3PszqC9TFR5xo6b142W84pmIx7+0OvNgceJkShxoXef18w8VlS3vVkMuHEymBabEx23SuElReT2osMuswxEQkqc6zSVFKiSEWgJbTmL1YFOz1sStdqk8SpirPR12t6i54F+rAbwR+ffr87wD/Ke+aCCRQbyLkzltR0DE6awQRxVpI55z0Wsyu3WW/AGBV5GedMNwpEdwFuXJu8cljHYB7IR48Aab8ilnVIl0nPgpJeZqmL6Tvgjsbic6a74Ir6doSrRhHcym4VggjhJ0j7pxlaraRKDLZdcDsAwfPgY4YXekiNCv3ljwBa+5Ax1R3sPWBs4RUZ03PWTOw89aophmtsCqbyHimuKPFPdhaMeUO3AR3vRe5+5yHIPSbwGOJgAL/kRgb+7+nXgJfzdWGVfUbIvKVx07y7llMbEm98qI5sHEDl2HDYWhStlxabZXqfBLX0rlveu0Wd1GKR0qCZfwbdXpdObZUG+6Yo1QI7dUacbZCTJ11zEMA/iDgrMimdhFp4u36plZrmxB+dn5aY99Y3kBwSu8aSLkQzbUV0OguLGBn7IXYeYauwe9S/wGn0KS+AbVEcPQMQklu6kdP7m1Yd4bOnZ3z96wK5GrJ+ft501vfR7HMQieR827g4qxneOForhsrMpoKfqyK78k+IAvKXPIGVl7rFHAkM8ksGwTjGud/S/BYIvDrVPXrCdH/uoj8g/te+FY7EM0GVp6lWvefHM+tEYRyEpBS+2lX8fsEoe5Y/kdT6TmSrxcbueW+K3rkDKT8l75bTYCwsZ53JSMuWCixVbm1CL9S8WY2ls7ntGY0rHRnoHTGkTaiZ4EhggSrGNzs1dJyUwFOUxdahhcOUoNZ10ZolDg4tJciYsdROB6bYpfZtuOssnHjYmn3XhODMi91uNQDMqgwRs/IFKLb+sBmMxLOB8atp1GrL0gOIJoLSadLv1JLYNaDUKdjZYxEL9aK0L5teBQRUNWvp7/fFJG/DPxa4KPce0BEvhv45g3Xvv0ORMnHfeaPBIRPDueMh8YkrkbNBZV04loM05XNPBn0sgn4rns/8gWd0Jx1teBe16xCJf3kZ2w1NeqcCmXkklauS7aAIFMDjKVf/ibpYKEWZLuB5kq8XoldIJ7BEEjFQY0QuCM0B5CouFFwvaf/QNA24l0oRkJr8ZWI1dEz9J7BWQeirhmLGmDpwlNzk86NhRDUDWFzS/i4zPll7lKMGyWOgJMTTp+fvUgGOhGAOndgrZbA6hpGiw+oIwPfBTym+cg54FIz0nPgXwL+IPBXgN8G/OH09z94GxO9x4TKx61YCvHlcQN9YmFVqu3Nm7ciACdI+G7Vgdn4C9GyzO1WkfyO4VVOT3JK3ClhUNwgNHs7xWEqQWxA24kIlDTcyhh4Wwrr0mWXg1/sWquOE1QYYqLKaY4+1SSQVAItNo6h9SU2QJyW3AMEM9b15jkYO4/uZNYqvnGRRgJjai7rUDZ+TMU5UlNSP9K5kTH60rFpqHpB5J6FNGoSUkHmuaEv/72JQECykaSMQamJc+Y3ScXIJcZreBek4DGSwFeBv2yNiGiAP6uq/y8R+ZvAXxSR3wH8DPCvPH6ad4N4b4RAlI0bOISG4+hL9Zxaj1waY27cyHeJ2Mtz3yYsJZFMmPI0Kw/BtIHuMYk8jgLOuvfGzopY6NF0XTfaebEx1536dN8cT++0RPRlQlCPvzRimmZQEYTkVxOvsBsZFaxSq93PH1OVoh7aC6uzhzQMUZCzEZzidqPRjSBoby5gCULszcqfxX9gajWvlKYuGUZ19KGhj57GtTQSS0+J3D05RIdzEefNa1F09pp2SbYDnC75rIbAHZKAlSDXourUhDTbMt42PKb5yD8E/lsrxz8BfsNjJvVGkImAg630XLKd3HtRStGKExtAoeILKWDGje/gwvU4j4XlfevjUv+t5vTQfVETD59dhaaHOwXXm74fWwg7IW5I1ZSra5VZVNt8mlrWvlYLNGNL4nJWykwJ20AIggRH7CBsjEu6z5XmqOhr0o28eRY2EbcJk4oRLANSIuYKZooBsGCgMEUUpvlm3f967EqXau8i521frhvC1Bmq9dbaPEg7f+ClFHDTWi9hRTCrr8m9B6Oa6/GmluRvA55ExKA0DURFjgNy7fmZ/kMuwpa+b5BBcIOUvm8m6+r0EjJSVaHB88G5mwDcB5acHYpInUXa6dwV1aCe2upYd9w/36vOocjBQE4Tx5/sAhKtfmC8tJyM4ANytBRgumjX9B4GgVZptmNpCb+cUx1rYTkJE8HVYGoCz0ZGGqRPRjcVXBCaK5tPc4W9Q/GMX7K2YGgqM+YV7RRdEACAY2jMz171LvzscGZRgam2wDF4ro6dNUNxyns7Kxrw6nrHODq6biRGx9Cn4oup+5kEtblCqWglAVw87TsIlGrM2WVqrzCpBApSNStZBm/BRACyijAR1ul3hVkPyvvAkyACOpr+J8cBGYTrYO3Gwuin8tAwR57b8Hp5/rtQxGounlWV5f1hnTDV18+O3XXPdF0JTjHCoI11Daqt2KLmLmwOgLMEGoBwlt10iYB0irvLhZgfJUsH1RTq59FNRBtBW0HU4ZJrNwcUgaktcesZmhaX6kRqY4Ffog6CcHW94VvdOdtkF3AyVUkaY6o0lWIIggrXx45haKz1t4+8PmwYR8/+9RZ6x9EpNJqqniuxsQcw74mmTsI6dWSC0/11A49ZLIGtzSL2ICqlynI9/E22mKXKcNebeRJEoEBq9nCMDcdo6cO++HFPF2xKla1+X7PKL1WIN5pbtvro6fGHChoLH/zs753XMtUizH0CG6sjEDbJfpKKkKpg1nooBUdilzIboxnoXBdKI5E7IakBtSpTIiURcx02WAVjgcxyRaG5NmNhuBbChTC4hriLJtWNYk1BeiuZPny+4ZvB8ezZgbONsepat+5Hzxh8cQkejy3jYHaF4Dz9sSX0DrnyNFdmcMwZhQhWUn2hGuUuQjDn7tkGUELMyyuczl2uUSEAYpWuQhSCQrMs3V6/1ryeTBGDs2FvoUBPiwiI+bMbF2ljnOLYNVPZ9CV/zpQ3M+X7SAdvCjUhWdMja/fbTB244ca1gXDtupP7S6m8dDJO4nTDuZY+BK5PasFAiR+IjaUbx2gXqkwVmvvx4VSylHwrn43yKKBdZDyHHEOrYmnI7aVxYYmecbAQY4TJJhChee0Jo3BdtRsLqd2YqtCPvpRzG0dPuG6gTzkNagZGPwj+AN2FEcbhuTCkufqDSUi51PiqJ6AYDStx/R7SQFmPTDAT5AQiuWWQTABy2nGdYHTb3n5SRCDHo3ssCkyczgzqhnSSlFFufiNLnftt2ATWbnWjvUEXJ90CD/EfR5kNp7HixF6JW6uFYKXGks47JD02CrozYiBjqlYsxtVDgDg4XHNb/Gzi9kUlSch7YvtMonZWUzol7FLaM0k1uE7ESoRRsWpIjUUbuiEFPg1CGK2WYM4fCNFx7K25SBydGRV7h7/0VjQkETJ/gPYCfCqdHjZWa3B4L72RIKUGg7qE6PeI2141OS2vS8RDAhCmNupRrV6Fau7duAI5Z0DnxOMueFJEQEI00Q1z8Wg0azf1P4GS+kX+XL3DJQFYfn7jydUcm8KBS2LQrMpxfd5SfajmU4ukd01xuUezcQ5XfovbaLrnaEgfB7Fimml8N1qFndi4UoZs7H1JPZa7NIIkdmXRvC7KqlgSkELptSAxBQSJVdsZzyBsbX1iY+vghjTXRsFBbI0YaKuQipD4JhQpQDUFQA3O1Iijw++F5spyAlSgvYTtp5H2Olr9/xee4YUSz4KFKjfJ0OyYpRSX9yBJgKkDzWTlHawtUSKAbgA5eI5DQ+NjaboaVRgXBti8jnlN1+C2+KQnRQQyRBXzBS/1+gXkAiEn0YHvAvImqKSL7CITUaJb3LuoKHZ8Vs48Z/GV7kQPsCssN2P9OVnZ4yiMpcuOFI6HWs9B9cIIRC/o0VvlYH+3cTCZGgpdq7sSFZ1dzXWoYK6/QGkAaj350rS9SQooEyHIBKBLtQtH4XjoYNsXRDKPhIPe4Y7OKgwfqtZiaU/ERhh3jsP7wtX3KsOXRtx2JO6bmYpZr6t6gbCeZTKzC9yxzURTJ6e9oz+2aGcGTiuJ7xiH5uQCAcTlCs7Tei8rX6/B0yICqaBHK4FGQmq2uVjzmnveBfd4Yfef28p900vyTSAX+Z2LctWp+eUWkTAV2ch9E2/qolzfHybWtURYMaQhJFHcaVGvXLAQW5pUh9BZDbwYsft6wXX38BBIauxanmmyAwhUXZeMWsw6/KjlFRDs/llwwqVYsNYSoiQIMasloyMcYPC2uuPoGfsG3XuaC48/mGTTHCgJQW6w5x7PoPeO/Vdh+OqRZhMYj97ahA2C7y3XobhB017zYWXPrb2H2yARNt9bJyTns2fASqPHpf0lERjR1JlodFUgF4VI3ARPigho21gVGxz70FkqwcztlbhaFr9rzrzahvwBN78LAVJdulktAFHabmS36RmTL7vxcTUqLJeXzr7249Bw6FuGwfTbeWvzTCDywqTPlY/eWGv13JragIuirRVHja1xXOnBRyXGSRyXEfzeEdRKgcUgaHT4xhq1xkVp86zT5imiclKA1cqJRWt+Eg3ZXG9c2g2VNCAT9x/PFN0YATAXYyKISlmT8dASg8f5gGusExIX3lSAwSQN36dCsd6u3X9FOHwloGeWuCSSXKHXbTKSWsMRiaaqQGomGpPBEJNaCiEd5lJA7plZpIpAsZOYuzE1jA2G+BqZejZmdaBSBVUouR3aO1v61FtTfO4wsw5PighQh2eWY5Md6qROXxaj7ysZPGpu6W2nghF1IQ5V4WzT0zrLXW9WEtQjkvoomPHn0m3Kb8FFi20vCK2mYJeSXGn3rRmhykbS5PYTQ2ogDBAOhiioIYjmGgQw24R2W5my3dLfzO0dK1JOpfIsm42U8dO/LKZr0rfrjEdRMwTGIMkWkB63UQuNjpI8n2mARIizmqEexp0Qu0nt6V8ovDfQtqHMSYMrgpSW95heZSIAmoq0LgOF1HMiIixfc8kiLBJQfod2b83NaMGIdw6JTwivPhGK3HRXk/pUVUVegydFBGQMswq5BarNNH2vCMDbsAXcNUaEIvMXo1i6VIVtM1oSizdVBnKKa+qamxJbcmvqoI7j2KTeiVaCO+bOIQKoFsaTe/rdHHhE4VKaoinVKWHjia1xPTfotOFDqkzsFB0gdFbww3WhdG2O9mB4b27EGB3j4KdAI62IgoJWFClXJdJGjZsOkiz/qTx6RQQkWr8E9RDGiZNn42BssKQfMDWhJlxJlYit/RvPtHDM8P7A+bMjIsoweMahQQ8eN0ppSFqImCQEFlMlVKu1zusqYuXc8+0T0ZikVK2IgE59DirJLat/eT9JEFPhkheMXPI9Suo1OY1/G6N7UkQAKIvvJJ4aQ27D03ctDdT3ztxIlKYJbNpxVQWoi13ExeStapIzsX0RgGO3S5tSqpvXqgCcbpB8C6/gExK1UpDfjJGmq9r0syvPpdysZOx0oeiwWX2JcQoyKmXYZh6YxEFJHLuNxK0jjCYWu6qIRz3X0rEHC+LRFLWnLoXmxqQVdZRKyK6JhF1kODeiaYFSSnwWSrp596znbNMzjJ5eG0LvcNeO5tJsCL7XWTfi7CkoS5tzLRZLWwQRZx+yq9Fei5Z1kWj2E8SazYqzEmaFcOZELl8NnrWEFAQ2y5y9BZ4GEUi9CPPCWrJIpQQlsW32ncWxdw35Xjk2PElsjYurzSYzTB1mLOElSwVrlXXndEbJhlIr232Ph83BO1m3bLTUvCt7L5ToWQC8T7ot5ncPUdDN1IAlBkvBjWNWEbhRDQvRoy2WoejMwh87JfaQYweiNwTTen+n96tJzZJcTk4VSh1F6zgtjfUY2Hvl2HYlUEqayO6sZ9sN9GPDrrMmoMOYelb0jmYvNNfgUwEUnM1HBMuwzGufGuPmJS1S2cozq8PcsHE6N0vvMw+jD4g4gibJzttLKBqewzw0TtHOBpPKBX0bPA0ikAvip26dZhhsLWwY1tUBbjj2TqHi1klnDumfi45GYnJvTumuVgXHnm9URwytGdlE8amVuo2TxixjU3kP6iks2NNMPK52XhLHc9ERqYx4sli3HJwDWOx/k3Vwkksu+eXvUsEU4iDGwbIxM81NMyHP9RCTOlDHVJiqIJO0kOJEtEmGVad86fkVL7d79s9bLt7b0HrLMOxHz7YdOWsHroeWzoepmKea/u2OluacuxCF1oynVnPQ5pyNd5OdhGIQjZJ1/dS9OCwKk6TPsRFCK2bTSEsmObYih3znykkry+i6qiRbGftmSvBEiEBeeHvQfWi5HDbo4KbKLtlannWm2sjyNijAXZxWMSSrjGpxFA6HFlWha0YOvrECGFVpLHssPakuU4eGxijE4Ke25dldWD/WEvmkOiaL3yv7hToIrf2WDYRFBK5F8mDIRioNnsVyk9VlcmFmpFjhija2ZBPHFJknxs1jSzHc5WKfMYnDms5RV0kv6W/YRtzWvDDf/+JTPtxcMkbPVejY+YHLseOb++cEdVaANKlgQafSXq53NHusDFpIdoQNjDubT9MK7kjJxLQ5JmOhULoK2XqZjcUnG8fkoHGpKarQvyfWtKWxAXKHYc0iZJLwZLF8sziU2Sa6eY8/DSKQIblj+tiwH1sY3BTwko0tYjHv08qn6+5y8T0WiouQghgaHOOxIQbP0Td4bzaCbB/I+Fm7BuvWWUEtemwcvKX1joZoMsrkEswiPkypwxlqSQDAC7nhvUYt3D+X1S59+JbEJSbNYxSr8FO1P7ccIC1BR+UarTh2PuzsRxkS8uf7p7iE/Nn6DAAewnZSW2IKHcZB7KJl/nnFbwPvvbjie198zlc3r/lSe8WgnvPY8dwf+Myd8brfcTV2HENTJLNcUSiO1l05JzGBhRGHDsZzs7vELqVAiyG4G00SE2xOsYFxa5KDRMs90IMtuagUNSe0QthC/wLCs0jbhVkL+DuDfxYG4Ptkdz6mvNivwJqMZPhB4H8PvAR+J/CtdPwPqOpfe9P73HMySUk0NnsxbLgabNdY6GnChZzPnZAl1xXQqMmllo1dempHqGGJBOk+03wo1lnjZpIMVWnM7CWIoDhCgCCO0acqvFmUz4Yhl4xD+XZZ3B/dFP462H2yeJ6z72Cun5aElpo4kPZNmLhYqSaUOCzJn57FWU35BXFjEo16CKopw26SNjSJ8Mapp3XNrr6aKGkK/Z13goYYxOITNHHarCFlZO+syIhLa+SbQNeNnHUD513P8/bI+5tr3mv37LxRsuf+wJk/0iZPzItuz8VgxUVCdPRjQ1SLl9DB4Y7TlLKHIuwgpAQmbUA24IJFIHKcS6exSWvV2DqG5LpVZ+8pNuamHM/MPTm8DFZFKW8psRgKREryU95++TUWubZiIvfxfD2mstCPAz9sExQP/Czwl4F/HfjjqvpH3nTsN5iM/Y3RAkiin/0skdRrftI3s7FMJb2nJIJa9NqCgyZkvs3AoknfsxuSq2VNInCVG6ApHn9JcEqT0KS6ZMISVyy8osa1XcjZc1NvQfLU8+cK8bKfm/zsiXEImLqSiWYS57Nl3k3u8uQOm9YGEkInYlsamiQLdoa4TUErktSibLRKA7s24r0FG3kfS/ONIpKLZQWG6Mrm75rArh141h3Z+sEMrS6w8wPnzZFn/mgI73paGRm0oZUxta03bn+NxVzkYh3ZRjAGC9uVvafZWx5FVoNik8KUizqiOKR0NZZgFv5YrU+WCCTXMlVjDhJNvQhbGJ7B8F5En420KVw423qybUGqNSsEICP+AunvI9++LXXgNwA/par/WO5TSfVdQVSIcAgN+6Ex8TTFnrtkLZ4194iZUCfxOS42OtPLKsE99eNV0sLkt55E2OygmJBTYASXuHyR3LJPt0bUjNTV7TPMLM/5Wdagml+RQtZO04kzS674Uensmqzgw7OagyexPLvXUiYfXqGL+C5adyCdXIObzViQu3H2u3dTFaBdO3De9pw1PVs/sHGB1gU6N3LmejZuxEtkUE9Ii+slpt8GtjKwdQOOaO3HibQSLMiKiJfIVdzQJQp3iC1XcQq6GhNxyWrXOHrC0VtAUpiQXyXZBHwl3TjjRbG1CEcd7XdfXJYyNRuVRAyiBYFJ1ERUbD21sw5Z1gy1Yh43veZHqrJviwj8ZuDPVd9/j4j8VuBvAb/3nfcirLIm3Chc9hsOfWtFJnICymAUutjl8kYHEJ3ETC2Hit5qAWbG/TLNsDEomFhH0ZnqIfkr+ZLaDimxRmyZIVyRDGAe7lnPrbr0ZD5J28lieLmoph75ulp3b7IVazqGwpjq+GkbDclbLWqKayPb7cDZpuesHdg2Ay83e543RzZuZFBHHxuOoSFilX4bF2gllr8uiVDP/JH3mn1Bap8Q98wdeemu2aZjOUs0pAf3KE6inZ8eKCDlvPJdHS/cAZcf0JknaesSwfGBq76zsgtSdS5OSB82tmASTRXQVi2eIhHxqFZpKHrBeaaSdnl9M/GsJbP8HqrjKGiQ1ENTplboQikhll/924BHEwER6YD/GfAj6dCfAH4U214/CvxR4LevXPf2mo9kdSAFW4Rk0Lm1RVRFAMr3WqROx/J3cfPjky6WXoVfIGctlpPE62T8KUEdy/lVyKyZy+QkqOzlqHC5IHm1WUsdxcZ84uJj2UC1CJkj+2DaXG0TZhvMJ04NJmSddwNniVPn2IadH/igveJL7VVCpoHnbs+56/ESCeo4aMshtlzELWAJXlbzwbh1hnN3ZJs4uiMyYBx/KwMv3IGtjAXx85/8PSN71vEdQksgYsTC4WgllCjMqI6BkVY8GzfQusDGj7yOmxK0BKR4BczN2NqNsw5vqpuJ6ep0ejdJWlIPMTI7Dsz3mBhDKvQ5YFWcej/zBklKilqL9yjFQ05+uR+8DUngfwz8HVX9yCZkfwFE5E8Bf3XtonfSfKTxaKO0uTJrmFJE1ZH08kl8I1IleEwi/SzSK0kEsZ1Pcan1yMBkAKtj6xOFd0mkjps4GQYDxZhXW3fUJQ7jJ6zXWn/2k7Ew682IfW6aSNeMbNqRbTPSOculzw03XGrE0SRODNAmSrTxkyEqn5uRCgzhn/sDz/yBrQzp2pGX/poX7kCvHi8TN7ZHNA4cvREDG9s4fA1BHZ2Eoq9HdbN1CQgHbThoWySHTEBqjt8z2YPy7x7Fp2iekObXC3jV9Fukc2MhbCE6+zdaI9TCCNQIgUX8pdDpMRl9fUp2Su9TCyEQZhWdMgHI6lTeS/V+SqpqyQXJ3qSZKqqrhr836Un5NojAb6FSBXL3ofT1NwE/9hbucTssytMM0RHCpJtn0Tu7bcAQLKZYbntZ6c1low1MYvWMKnAi0uXx1GOJHAVhgcZE6AC4xlw+1jePEvI7lYGCbLRsmmBZdck1uG1HumakdZGNH2l9KBs3i9gbF9i4kZ3veeaNqxbOWCF0rSsDRTzuJBQEAypROnPZkU4CWxloZSzIbt+D6epJDK/19nyPNuXrevSEEARcISC1qN9i87QxjZA4na7L0gZgXF/l5B5ZXZhgeu6t6zlzZkB81hzpmsAQPMPgrXpyvV8aKfsJZ/tJQhL7m6RyDtN+yxJBce/Fld/qfbXYakvIIdcWH7AIAqttRvfwCNTwKCIgImfAvwj8rurw/0lEfjhN66cXv71TkP2R5spxsd+i0RHOIkcxjqKNpZtSIS2tmtgspoMxCjK64tPXOrintmhnl10KyxSw5hRuXi2naQJn3cCuNav1xo8mSrtQRFIwBPWiqfddpJXIeXMs7qyoQitmJDtzfdGZOxkLAnvRgqStjAV5gJkRzaMEpPytoZVAx0QIBvUM2hRu3lUEJZ+Xn+FKO7o6dhZDSpiITA21xFATqJmuX823nkt+Kfl4hproFExVC33wacxBmxkB2srAS3/NV9vXXG43fLR/znFo2AdX4iriJjImt1wOYIredEKXkpuiaPHUZFfqNBcm9TCrgMslyb+lHgrayLTfMJtXzs6cpIB0aY30325JQFWvgS8tjv1rjxnzDSdif8eAKGzagV03cDw7WvSXi3RNYOPN+OOwJpNbP9C5wKiO1/2W66Hjqm8Zgp/pxCLK882RRiLeRbZ+YOtHdn5g40caCeyDxSXUetzGj5x74zKGxGPShydDVtaLt24gqMyQOevGWa/N3C0j+hrUiGXzcQSZuGNAyMlsbpFknhG7iPNprqFYROdQG92sh99EBDwKMhJ0zuHtvrFcVx+vIV9X1Im0BkFdITWR0+tqgmNSiZ/dw1QWm69LhsStDHy1fYWTyNeuX3I9tFxWdha8tWsbGmguwYmJ/UXkL+5lSjxK7WmaGWEre0D5vY6+DGbcLk8x2torrqgF5M9wog68CTydiEHn0d2G/mXkv/Phz/Gy3dNK4EVzwCdOs3QjZYg4ruKGz8czPg879iHprmmhPZH322sjHm5kKyNn7si5O3LmjniJXITdTAzO99zKMBNZgZnLqkbsWmStDWZLxF6DJSLV3zMiLzm/X7AjQ7LU5ecOOJUiTomST+wtz79NRAEmrn1z6pRx7fnc3OzYKqyJ1Sv2hwB0AiGtwbk70rWf8X6355PmHOdTyK6b6glqC/HQANa7scRcVFJAuWWF7CXgT+aEIV9eBQSeGrM1nZhawBGTq3DNFf+GrsInQwTEe3TXEV+M/Hff+ym+q/0cYKa/1nosZHFyEnfpKBZp0y8nDrx1/YyrZQNWFoG3MszGqs/zKFfazeZ7u86adOR7cMy3BWGFqz4cTt13NrZ9Dvecf83hl8Tm/jNxk8SR3scSAoKriFJQZ2pYM7DpxmKTCcExJiNh2Hl0NG+BVWSWWUj0zP2Xb3SD7l55hu2rZu8AyODQRQ5JLUUsxyk3fwNC8GSIgI6DaYtd4Hu7T/je5hUH9XO9G00IZ+nGASkGLcS6Gbcpk6+vNnNG0npzZyStfdXIiNOpxHUmAE4irZ7yvJoA1AieCcKSEDwW7kL0Wr9eA88dG0wmnX6J8F7iqvi+Ps+KgFTj3Of62v5R2yTWxvGVqhWTFPdes+dZe+TZ9sgQfGlVdt23DEPD4YUjDI54dLiDwx8t1yF7BDJnz3/TskzqQIZia2LKKyGZnnrLPzBDpM6RXJnyXpYIX9/0AfBkiACqyBhBzaIMtqkP2s7Ec0/E66l4HbLenYhDNkS1Mp5w6czxB9aRpuZASCTcgVzTHCaOv0YIHgN3EYD7SBl3EaL8nFmSWo5/X0JWW/uXx+4zhkdXicFyLEd+P45ePb16zlzP8+bIs7aHFjbNSJ/qP+5TlOMweIZ9S5QGCa4EpGUoagLMOLesqAdrMSjFLhAqL1P5PbkcAUr4tp4SmQfA0yECMllQY3InDdrMOYC6tGhzX3btR+4TR5h82g6vp+L6jVzxNr19iYjKCZG4iRA8Ft6lKpFh5qqrpIE1Ufym698U8Wfj1OcvbDGzMbUBLJ9g0IaIY+Ms5+C9zR4nys4P7EOLE2tgOnSe/dBwKdCPjthLyquQQgiKGxFM5M+fU3wBVeRgwd2Fy7mUD/Og6MT9oTqptjxWhswHwtMhAqrkiMFePUOi7HDq/zauD772KVduMeO+c9sBWcy9ASFzoEy5ptrQIUWv1ZZ+A1es1DAhyxoheDuc+vYx7kLWu65fIn9B6HtyqGyHgfmzvCkBczJX15ZjZ0Jgcxb6ZHTc+YH3u2u8KI1YPMbOm/fmeuz4vN9ZIZfRE8L0vE4T0mrlAdTJhlfqHFSPU9sOpgCjyctQF2TN403SQc3+kz3g5ylY6BcOiFV2ya6lAV9Ug6U46lEzHmV/jjp6zIaQXXQwxaBnSSHDUu+NySddf68NYtucjK9QJwTMXFqVASsTguU93yXcaXm/A2aIWyH0facdFu9oGax0H7WoPre2B62Nm4/HRLCze3frBl40lrVo0Yv2eesGPhvO+Mi/sBqP0XGlEGhRcXgB30vKPUiPnqL/gJKBWJYlIbguiYRnxuxLHkod4l5gjRA8bJ88HSIggjqHOLVAmWql+oVOXiLlkq8eKl93giKKp2tdjj1O3Dy7v8qYyw1aIbuX2xxhc1gSAlY447uCu+5xl2FwHrk3F+vvqxJkd+bcxflwm0htS6klqhpqItNXBHAjIy4xkxzElN3LOQX5emzpgzU23UchagNYvoqrG4lSefMWtoAaioTgoCR4ZSNgzvmoxAbNdoAlZOPhA+DpEAFVJMZUYz5zA52J5Nktl79TbARzOGjLQduZf/66SjnNsMapl3ptvt9NXLa2YOfP2UrvUYbFb4+BxxKSJTFdwm2SxH3cg4VQJKW65tgZ7lINStiw6KpUUM6rYjpCJQV4lDN/nJ1bq4tOIhs38qw9ctFuuW46hjYwbFLF5cHPIwZzPEAdIFT/5uZ/lyARtA4wEKbMxJvgO5YIZNAswp8Gx8AktvsVXfvEtbUSy34TLKPaJpjsErfB2rWBPM+773+n++6fEsgqQf1e7msTqO01OfoyH6+TjUIyHtcEoE5COh13zjC2buBZ0/Net+e4bRijI0ZHiBC7lL1apWSLq4yDMCF7bQysPtcZh5BV/YoQLKG2DXxHGwaheAfyy82cqejdVfRadt+tQQ40ue2cJWR3YR1oBLWx7XYisNSn63mv+d1P4B7zvG+wzk3wNqSR26AmAG/iEcnIDTk24FQqGCo35pIA3EVoo5oauJGRD9orXIo5caJ8Auyls6QjNUaTeyWoTgViZgE/sCAEuYlI5TFYgamd2wLxv+NdhBXE/FIrw9QcqSskX7m2/psX9i6ddoq3N9HzvjpwhjXf+Jqx8Ua4xwZ4LBLfRUQeq27UOnp4oHELJlvEaaDQNNZ9VataFYlp32S1YOss9DwncuW2cZ+JcjE4NLRojv8XTFXNwURV9akCuTBNJgBVzQn7IZsIEsdPwUWpjGkeZDr5O9YwCMUCs5a1NkPqnGFWnVvbDuZwM8G4CfJGWyaz3Abl/sv73PN9Lo1xa3DXHO5SWb4dsBTXMyzzL24dY6bSTR9z2TGr5kLaC676frcq4lHalKL9zB8sDyUh8caPHI4t/cGjXqa6klKEg/T9NNJvWShGc2sxmEn6dbs2CzSSuUTwHR0xCOQWzda4c+56gyp6LBGC2nAIp2J3sRu8gU6+JET3cb8tXWQAJM52HwS4jzh7+/V3WP8fOf5dsKxDMFMJ9OFBU74i3vndL129NTiJDKnIxCwcvMxPStRpl3NHqsSpnR/4uYvn9G03GfrqSMAs+tePka39C7tAiQWonj+frrW1UZcSAfdmHBmeDhEQKVVwc7WYOp8+G3dmOfZ32ARg2gx3Z6+NZdw1m8BDJImlVXyVONxy3U1wFxKtSVAPGf+xUIvttcUeAFl8vwPcwjuwXP+H2ATy3pk8BOZlstyQyHv+Gjo48z0/sfsyV9sNce+Jo0xl4YATyT178wpxmNx+NRFZuv5n3wtxWEgED4AnRAScpVnfsQAhWXnXNvxNwT/34cRGNtwJwgb1qwU8lnDTOWtBQ08Zli67DA8hAPl8t+YdqKSMqFMWaU0E1oKJkElmjCpEcSX+YOsGAgecRF5u93y6PeOqba0wyJiqSse0NytCMCMAXm+U5NeOF4/BI7wCGZ4OEYipU2YqypEh+7Znxp2KAGQE69WvILEh8KD3Ccltb/5toWeuQiWVnKYV3+8N31ZvAJgZDzOXrLmvf6zh8B14D5Zj3pbpmFO3YaoFYcfnIeNAkQDs81wNOSapbyNTifMW8IuxQ8UoOhnxLnLW9DzfHTg8ay3TsE/rm00Pi+A+gOiy0TAVMc31LGvJ4S5CEJN9YBk6fI+tc+dbE5E/IyLfFJEfq459ICJ/XUR+Iv19v/rtR0TkJ0Xkx0Xkf3T3FN4e5KaQQafY/zUu+hAr+bcjUu/nE/xNvuefJ/BVxaXZ8QdGDcaFalEfnwKDYmr/Hkvlp4gwRJ8KmPiTf8uxM+TaEL/q+df51R/+LN/75c+Q93vCzoyOuViIKFWVaLu2VCPijex6j4b7SAL/D+D/Cvy71bHfD/wnqvqHReT3p++/T0R+JdaD4IeAXwT8xyLyy1VXkum/DeCIt3Kn2i6wlAJWRcJ3CI8V+e8zxzoZyVdrUwfVPGoOb3kHuyxHV3Bn6PIsSlDKODc9m8eQNGIdhC51y6DeisA6KbUncCaFtBqKetiueFO+r/uY9xuLIdgPLR8dXxKDWGVipmhyTR2wZsLbSqepbwfcSQRU9f8tIt+/OPwbgV+fPv87wH8K/L50/M+r6hH4RyLyk8CvBf6LtzTfR0FWCWbhwmndlynHZdPk399xoExOdHoM3DbHOo7+xusficTvOpjoXnNIevoaMYCbk5FybsgQPcfYMKZs0pxAFHDkMuwZvE6JSWZbcEVa+FJ3yYdnV7x6vuM4Ctqb6iDj1HlKVCm9g2VKHFoTzrSSGt42vKlN4Ku5rLiqfkNEvpKOfw/wN6rzvpaOncBbbT5yC/gbinosM9xqCWAWfvpt2thvK9Dm5vHdKkGrEf/xWYSPW6t5HwE5GfO+0tjsvGpZb1Mp8jWDGhHoo3UnjqkvRe5TCDBIk4yBkzSQCcXn41kJJPrq9oJPnp/xSXAMhwYGB72DYfIa/EKAt20YXNvJq4/6TpqPVIXYrYy3VRryC1/ATRb3TACWHNGLvnVR9xQe/yrummOoKiGtF+94HBI/1IJ/AjI32i1bjT2UUM4qHMtU/7CWFPI9aiLQx4arsWNwnuhdKQmf55ZL0g3qZyqBlSmzObYSeNle89WzS1SFi27D8dARnAc89DJFDmZjXkyxS2uPmaMKkbcuDbzpzvsoNxkRke8GvpmOfw343uq8Xwx8/TETfFdQNpTWm2HhImQKO32sqHuXD/5tEJl71/DTdUnnsUj8aKlJTwuVZuR8aDLxsqDITRmJuZRcDjOPaobBPhhqtC7gVIgJ83oamtzRGOutWMeCZOKQP39pc0XnRz7bnPFqv+Oy23BsOuK1x/VuVmW45BV8m42Db0oE/grw24A/nP7+B9XxPysifwwzDP4y4L987CS/nbBE+GX477uCb4c+vcbxH8293yLUBKDuafAQyBLevDzbqeRX3ydWyBvVMaoheFSrOJS9cEFT23sPx1Tnr5VATJGmUYVX4xlD9IxqEuXGBb6yueS99sBn3Y5vts/5xJ1xrVuKhqFAajry8wF3EgER+XOYEfBDEfka8H/AkP8visjvAH4G+FcAVPXvishfBP4eMAL/5rfVM6BaNIKDWttp8/NPpcZ8Chy5y6e+5JZvS9+9CerYhjcR1R+KzG+ia98FN+X9r4Xg3jyvOQFYJvLcGXnpqv4HK4SgnpchsKkZh7BlH1o+7c84pIIhffCM0bNtLEloUMcYfUJ6Iwg7by3T96Hl0+Gci8HqThxCy2W/4azt+aH3vsGvOv8aAcdPn3/IN56/x888e5+vf/Ie4aqFICVgqMT/LFrT1x9mrzoXIFkJMT75fAPcxzvwW2746TfccP4fAv7Q3bf++YWbqs3A29ORHwLzRhunnHrpN39TuAn530Y8RI67X463JoqvQV28o0Tx6SlBvH0O0zpGiaB+qh/JnBC5dI9jbDnGhn3sOISGMXkJctShC9HKQqh5CIboGaIxlavQ0UhkH1pe9TuuBusv0QdPPza0qcnpmTvyIjVy/bC9xEnk4rDh89GhvcM6DGExBd9mieDpRAwuIIt5t1b/rX6vs9fy9/m5b8fodfNcbgpumWwTt1//cIK11LsfCzf1BYzF/uJxt7EmNxIq/by+NlZ1AO4L9X0BQ+Dq9+zvvwhbrsKGy7Hjeuw4hoYhVEZUFUa19nXP2iNOImP09NFzSLaDy2HD58ctV8fOaloEh4iyaweiCueu5xc1n/Hc7Xnpr3ESed3v+EcqXF1tCdfCvUuDvWXL4JMlAjWEFNfvKhdULZ5HXa82sxzjUaB3qB93ceg7XYD3J1JvWwIoY60h/uL4bUQ5c/GsYy/HeSjECvmBWSSGl0hUzyFJAZkAXPYbU9HjFFYdomOIodQNaJzFDvTRqgodQ8Prw4aLqy1j31hzW8C1kYsm8NHxBV/fvs9zt+egLa2M/GD3LT5/74xDaPhZFa4Gh45+qiNYZwllkf+mWgGzBMKHr9fTIwKaU39j4u6Tyy+mAO5caWZGCBYE4G2rAnf54NdKi9cIeld3oDclAvAw7nqv8W8iAPe4LiSjXDa0wSIB6I6AqNYtqkKrY6hUqqms3GTB72PDIbRcjx198CetvTWFGkcf2B9aa2oryhgdQ/Bc9y2vL3eE1x3u4IpZIm6Uz4Pwd/kuLocNP3H+FX5w9y3+2e0/4Vwu+LnuPV5uvsxH7nmF6NV9b3IVLgLdHgtPjwgkyMh/W2x8DqBZ6uC1Dxmq6jKPmc9dSLpiDX8Ix753Hb63wGHve4/V+9wyzxr5l9JYvKHb0wzEzXyJJ8lHccoszGHTx9iwD60RgaFjTGL8fGJ2zMXFPomO49hw6FvCvsHtHe1rhz+QEoFgvHS8/qzj//vsGf/gxVf4dd+34VdufpatG9nK1HoeAGfJwBImj8T6czK5EpfnvAFheJJEICA4Js5fl50CVolDlgIyLKvLvOuIvqkS4Q1E4M7rH+gdWEHUW/X1e0Aep+bicHOo7uq8qiy/tajB28BrJEq9fivXq81jTB6fPhGBq6HjMDaE6EpL+qhJCqjWxTsLQx6C5+rYsT+29PsWufY010Kzh+YK/FFRD7EVYiOMZx3Di5a/1X0vP3D2Mb/67KcJOBqJND4gt6QSz6z/t72iN4wxeJJEACbx/qTCkNxtZCvnP8C19VgYVgxfS8R+LJLeNf5jYYj+lIPXn+8o9uCqqLy5RCb38o6Y+jd9t3Hmz+tQRnyR7o6hoQ+eIXj6cS5tOKGMl1WEfO6+b7m+2hCvG2TvaS+E9lJSH0HFH5Nan8qE+YMZ/l7/3HP+8xe/lGdfNU/Bi/bAeTfwubN04NnWXGs4Wn9eugJrCeEB8LSIgAiaCkV4GfASOSxagmfxf9DTGPWbCkvkQJLc2Xjp6lq6G9cCXe6SBNYs6zchafm9LqCZuuXU182TouaceWn4XCLZm3hDLNgmd/Sp5pZ26yZ1YVpDbk8s19tvrnoOma2xFy1j1tV+sqQxqGdUP3PzTc8ZaSWW3y/GDZ/3O676ln5sCMHhfcQ749AAMTr2wbOn5WzTc3XouP58h1x6moPDHaG5FvzewhTcyFQVKNjH9krxA2jb8JMvvszf2X0fP/z8a5z7I97N37Q6NQNfbd64S+yXxd/l51to6NMiAgmCOoLM9csMA77ogzch5puoAmv6+7tMP16KumYKlRmBuEmM/nZHCWYOfFukYiZMcUEAg54S0njDhl4S4qhSIv+msTxjjhtQRx8ajmNDPzb0vScGzwBmA/CRpgk4p8VOcLHfsr/a4F43tJ87mj34I/iDcf/idVZK27Ha0O8PcHjd8fHhGYfzllbMyFge6W1smQdKBU+LCKhClJT3nUNBp/JRNSz7EtbW+TfR/5fXPESffxuQuegaopWgl5wbfw9YO/eua5dSQF33v0bGm8bM3Hntt0xIbpNYYiVN5ed12HMM6R30sbEgIih+/uPYMAyeMHo0CjomA6NXwsYkAwCNwjh4+Lyl+8zRfW7IL9E4PjoVD1FH6S1YIv8cuAHcwXE9dLRu5H030rqAzpKJrEKQyA1xA7r4mz/L4rd7buOnRQSihQ3nuAAL963F4Cokteb2EqmLgy0z1rIoumxnnsec+hROxKD2KOQSVe8SbjO61br2GtyG3Lf9dhpQVRO+mvAmkT1LLSuxAjFF6eWY+7UxXcoKrWFp4B2iqW3tojpRlgwaiYxqzURzXMBxtCIiKPgmMgaB4CAKUUC9EAcPvcNfOtpLR/cK2kvFBUofQW3SoyZDXiEEmQh4SYRBaX3gA3+FFytJVmDJwddE+tmxinjU5y1f2y0E4WkRgQpCihg8Ee0rPzRMQSMwbc46CSWf32LvtyD8DW7DE+LAu1ULajipxCOnUsESltLCfaDW6e3vqQi/Vrt/ICHtig2l9lC4JDnMiYEwalPuWbi86GIssffpKEgzJuKSx++DLwTg4rCh7xs0uf9UxWL5U/SejmJBPNcef+3Yfct0//ZK8b01FYnJC5DF/yUCqzeXX2wgbEA3kV0zcO4s+rDzpnJEx5RWjMwJwppNoH6nmo0Qt7+7NXiyRCDDvPKvqQjLiDSL3Lb+9a0bZ3Hr+fwMTh1RIm6h91u32pvdew9qyPkIuEnkz8Etc+54P3F/Vrp75sI7NWBmArBmeLTzVzh0QngnkdbplK0npv9n3R4oUXt5jDrXHygpv3kOYzWXMXqrExA810PL9bHjeGitdVgUE8kHQQaHO4rRgdaaiPhrobkSmivF9+YByK3FRe27ipDTJ7J0EFvj/kYoYDhX3PnIi/YAwCG2OJSmDYyNx7oQc7s4vyQAq7/NwghvhadFBLy1Js/gJOLVRPshNqVqzLDQHbNV3S+4e0RmBMMlpS+qx51wv1PX20PccW/D/XebB2DJ6R/ix6+R/+SeJxx7Pf5gup8zRF2oDmP0iVBQEnhGdZbJp1UyT5bSmD9bhjE6NCF/iCb21/e52G+JSbcfB2/6f++s7FcQXC+4Afwxi9mm7/uD/XMjyehXxQ8IpeeFCuAgdMJ4BmFrEoB6JTYwfjDy1Q8u+L7dp2zdwNW4sT3Yjhx9a4ToLm6ennemAQjcWJsMrArxDfB0iIBI+mdpub5wDOsqEzGD4VRv3ghBKwEchRDkoKLMvafAFzcRgGwBos44NLgpZPZtcPnb4CaDYP7tJgS/dyDOihQQF5LPpP7I/LckbeW/NdTn7UNbuPcYLZBnCJaooyqM0ewFmqL1cjCPfbZxVIUYLYEnhnQ3lUkvH519VjH9bnS4vXF+F4BoBMAfzYiHggvgepMAVOy7pP6CafipxbjHgoN2MDyD8UzRVtFGUa/sPtjzz7z8mF+6/SZncuSbvMBJpHEREa2LY5lx8NvgyXk6RKC8Zfha/yUOseWz8WxWJnofOvah5Xps6VOyytYPbP3IeXNk5602nBPlmH7fuJEz38/q0g8rLyafn63S+Z75b+PqXgdzkXhJIJbNTpbVazLBWXoElvH1yw4+uXJPhqXI3jqd+eqz+2r5LEDJo28lFOnq1XA2S8NdSj9LySTr6obcFoJbEnZCavedkF5VGAbPcEwJOtmCbwsEPq3nIPY9d/PJYrWo8YgmwujQUZDeIUcxpD+YBACG/M0e3GCWf3K58GT0M54gRSKvbb6axP7YQdgpcRvRRpGzkZcvr/mB9z/h+88+oZWRi7jjMmxpJfJie+TQtxyBeLSKoxKkmj8zNWDW5bj+LjJJBTr9ILcEaj0dIkASyaCUjCpVY9Uyvi6GDZ8dz7g4bhhGj3ORbTuybUY2fuSDzTUfdFfsvO2GQT0xCMfYnCBMhoz0m7qYBXMEjZnrJBhxlU5riLd1Q4mOz7aIDAG4jt1K4MukyszEf53X0AMYVYr7rQ66KWOh7ENX6d5TAc28lnm8UR17sWSdvLbH0BAxbn0ILUPwlpIb3YxTD6Nnf2wZ+sZEcQCniFMThRcWcEm/CRCjGAGISez1gFdcE3FeERfRFOdfi8XOKd5HRGAcHaPzhGBuOKcy9QRI/9wAEnQq+VVZ+020n+IBJE6SgHohbMz4N7xQxpcj/nzEuchu1/PdL17zS599zHd3r+gkcBG3XIQtg9qcmybQ+wolM9LfJMo/RIN8TLCQiPwZ4H8CfFNVf1U69n8G/qdAD/wU8K+r6qtUmvzvAz+eLv8bqvq7HzDVx4GzTdNKmOUJjNGzDy2f9zte7bdc7TeMQwOiXDWBtg1s25EQ5/3mxugZE2ru/OTGeYgVPYvOOY99WWAjR7oFWXFd3uJXz3+P1SucgqBOw2Xre5b5zewCk17uxBKvgyj70HIxbDmEhqthUwxtOd9+jI7jYCm1rQ+T2B7sn8bEyTFcCoND980s204b0GaybKtT4+ReoVFoIji1oJ2NEScBxEWaxgJ6Wh/wTglxkhwAXMoDaL2ZbV9d74jBEbLtSKGE6yrIWAX7VPhXdP2NEDvj9kTwvZ0bW0P+sFPCBsb3ApuXB16cHzhrBz7cXfID55/ww+c/w3c1r7iKG77Vv+Bb/XM+PZ5zGJspe1HlYQh+G9xGRBK8afORvw78iKqOIvJ/BH4E6zsA8FOq+sMPnuzbAOcQr2xl4CjtJMrO3E+k7rCagj8aYnDEKIjsEFH62LDzQ3FXtW4Si2/SrZfGxnKumEUc5pZwnzkuwTLkkoyf9fdaUrjLap8lhFrUz3EK5X6itBJnpbFyt50suvfBl3tmyK60w9hwud8UnTuvWRycldKOYshaT9WZCE6KuCujdpHodfKpk/5mEV4S9/eG+M7HErUnWaxPElhO9vFJ3cquwGUm4BCMwFlQkBkDXe9wo9i/kEJ8QzH3zKz/YJ9N5zeEB/suwcT/4ZkyvAywC2yf9bx8ds2Xz6740uaK79m+4gc23+L7229xLgMHbbkIW76+f8E3Lp5ztd8QRk+s1ZwyCR5EFGaEK394jGFwrfmIqv5H1de/Afwv7j/FdwBJDVCxkgqZm9UI61B2zcDL3Z7GB66PHf3QEMbE2QbPpZqa8Plxy7YZ2TUDZ01P5wKXY4cXpXMjrUQ2qS/9UjyHdQ5uv00xCRkxozoaZ9VrM2I6lJ3vk9sx2yFyjcRJgnDMdfasguTIu2P0Mx95Pi8fG4KfudByFp0WaQPG4BkTV49h8qWrJtE8i+eK6emiRUcXrzivOBcRp3a9V2QzJqQ2U06+3xJxM7JnxF+CYpLIGG1l8tzyWPmSIUAIjnFMNoWjRw7e3H57wfUW+utGUwMqu6+pAbaBbHw/EYLYKtLZI4ctjC8Cuw+vee98z5fPrvjy5pIPN5d82F7w5eaC5+7ARdzxiT7j68P7/OPDB3z98j1evTonHpqpA1HeP8UOwPz4PaEOV77t2rdhE/jtwF+ovv+AiPxXwGvg31LV/+wt3ON2SKK0dXTJ+viUGjxUiOAlctaacK5AjFboMQyeMHiOh5ZLt6VpA107smlHupRI0vrAedvzrD3yvDkW49jSJhCRSiw30byVIRnn0jkLn/mZ6xlkqmWfjW5ApY9P446pzt0ywq6PlhV3CC37VDAzJL28zE+Zicwzd2E0Dg8U7uuc4lzAddZ0U0SJqaCGif0Z8apXkq6rOXe+X20BnyF6OrbGs+rflkU/WDmmFfcbhoahbwgHb12Cj87Sfq+E5gCuNyNglgY0G+KyLcBD9GZwrKKaCVtFOy2Gv+cv9nzfy1d8//mn/JLdx3zgr3jpr9m6nkPs+CQ848f2v5hPhnM+Pj7jZy7e55NXz4iXLTII2iU1KLLuFZhh9S1QxRjc55JHEQER+d9hVYX/vXToG8D3qeonIvJrgH9fRH5IVV+vXPv2OhBptJ0VI3F0XMeO69gZQsSG67HlatgUpOhHby6o3rLG4ugmgxMmjYXec3QtV942aNMGmiZw1XUctw1963nWejYu0LrAzpnNYGkvaCUQTuLdJ7djTOJ+bmHVx4aehmNsil0iGyYjYqmv0ZdaeMfR9PHGRVofUiksx5ies0bqHAN/E+SimM7ZHrJMuolQiSitswy7+j5RSWqWpio8EJMxsI55cc56OGR7gY1px52jEJglQmciYes7J2JxIUXk583PHKMR+LhvkIPDDYI7ihGBg0kAEiZ3X6pNOoUxyEQUch6ADCAdaKe4D46cnx358NkV3/fssyL2f0/7GY5UywLhIm756cOH/DeXX+Hnrl7w6nrH9eUGvTL7iMQkUDUVAcjLEKv1eAeE4I2JgIj8Nsxg+BtUjbanHoTH9Plvi8hPAb8c+Fsnc3ybHYgSa5ExoqPjOmy4DqlgZDSu+LrfcHnYWIRYMlgRk/84i7OZewhGFIaJy4XdyNAY0RiDY9+1fN7saMSQ4kV3oEkW886NbNK/mptHTfp43mFqaoHD8Y3DCy6HDYfkK8+2gCH6IqbHsvEp7rMYze/tfZy5IYGkLydPgFO6ZixjAIvP0DVhpg5kvTufG4JDvdAk5M6qAuRglAkxa46dX24/NEX8r6WBGL0hcQtNKtox2xBqal7jgz1PdIzRDJCxQpASIzD6KQowvUd37ZIbsBL/k1Evx/TnlxK9BQOJ6uQaTJZCkeRB6MyAeX525Je8/xk/+Oxjvn/7Md/VfM6Xm9d4lE/CMz4Jz4gqfDw+5xvH9/h4/4xPLs45vNrirjxumEqLS4pLIMUcPBpqCnwLvBEREJF/GTME/vdV9bo6/mXgU1UNIvKDWPORf/gm93gjGAMcHB/1LwALPnk9bM2w1bcc9h3jPvWESyLXpDJl63RCgEwYsm/YecKgHAZH37ZcevMqeG9mx4/8M1pvceDPuiPP2iPvtQfOmyOtBC7DppSxGqNjVJNGsnRyGJqCVDfpyM4ZUjY+0DaBrZu4dOcDXVVfL2ftZUQX0fJ7duXVATgZvIsTIusUDZg59BAmH/4QfEHC/HtGRDDaPIn7WAYeCVmDpHRdBReJwSMC0c3HsLFNWjCvhKkiNacfR0/sPTok4hoE6VME4GiBP82V0FynLL5RzQCYOGRsp/BeEHMDOvvshklVYEwiT3YnqtD4wMvumg/bSz7wl4D1vBi04Z8MH/CN/iVX44ZvHp/xs5fv8a1XzxguNrhLjzsmApC2Wi4rpnMPZ9qfTPuyLAynupMujul06k3wps1HfgTYAH89BSFkV+A/D/xBERkxe/fvVtVP77rHWwNVJAj7YPHYo1rlmMPYcExGQAZnG2Tpk86cQNNCV4Eo6hQZHCqKjhCOjuA9Y6PFx+1T3nnTRC6PHbtuy2ftGbtmoJHAqJ7j2MxUkmPWVUeHb8x4VlvBXXJtZS6eRfHWBZpEAJbFNWDy5Q/BM2Jiu1kg5hJAJgCZ++c8/VrUzszEic4q7xQvQZQiQWWbQE6LneIWKqLgtGCfGRojoh6NVuvBiSsuxQxOlCBSxs+eCRQ0OPTg8VcO11ugjBsxDhvss99bxl+7VzP+pQc1AiCMKiXWf9yaSB4be3AZoTkIHBQ3MEUGJqePRTQaGnlRevW8Cud8Oj7jJ6+/ys9cv8/r45bPrndcXmzR1x3NlUt2CCneBxVzk4ZOiBudHc+5RKuw/GGFgNwFb9p85E/fcO5fAv7Sw6bwlkHhmGrB535yrtIpM5Ln/O+8gVUSVY5GkUt0mFPwifqnFbcXo4VAqIDuHDhlEOXoW678hqaZJAUnOtOHw+gnsTiN1zSBTTJGelEaF9n4kdYHzpq+JM/AHKFrAyRQovAyIQiJo5+EEycCEKIU9aIYpAviGsfOHpRs1c9ECiBGW5Qi4mcbQiYmyd6iUZAU2GMTEGLwldFAiU5nRIMoVuIrq2pRjNMP1rar6XNij/nso89EwLi9C+B7pdkr/hBxVf2x6IWwcyUICGfGv9BA7NRiARRiJ/jOpILY2VTCFnAwjp7LwdTPIZUwP2jLTx8+5Cdef5mf/fw9DvuOcN3grqwOoT9I8kQw1SIQyzdQUWIqhjXT42vEfhuqQgVPKmIwQ/Z570cTvc1Y5CrdH4gpUkxBkeKdyceKD1uzrDa9Bcnk2WmJUtRoL1Ad5gN3ypD+AhbRJubzBpDk324a4+6K6cObdmTjAxs/mkEycfyowlh5Bmpxv06vLVw+JdBkRI8qMwNlVCnInzl+NtbBhPyannVJFCATgnw+hNGDaIlWUJLNNhtdBzPAhiZZwfI/0npn50k0Qu1GQ3Y3koJ40jsbwQ8m2vuD0lwr7XXED0psJCHXFPEnqibSDxEZ0xo4EO/M4t94Qmvv2B9tb8RGiK2iDYzeEDM3II6JSKhXxtHx6rDjo+0Lnvkj17Hj9bjlp6++xNdfv+Dq8y3sPX7v8HszSOYkpPwsuRyZCriQGFTaSywJwZoKsIS3LQn8UwcyZeRlRDgMDf2hQY8pW6z2ASdTgAxMmzD9VvzFinGJE/1rktNkD0gSK72CT3aGrKY2EWnN+u2bWDhprmXnBMbgOPQtvfMMraNNenCOKchGvqX1fC1DsCB2JgBRZvECqnObgapxtZJcI5UfP+ngOUMzjPa7E5M9Y7REeitlkO6TU3NzLEFWswahLApANA6b38sUupuz+QzRcyivufHsrxsNuf1R8YdghuHGIZrF/aRjixn5Jn+7QgBHRAfB95HWOcIo07WNgAhho8VboN72QmyVuDUCHwbPZ1c7fkK+zCfHc0Z1XPYbPr3ecfX5Drls8NfOnmOfXJJDZYMeTUVRB66RQhTUJQl1ub/vMvbl9/cAeHJEQFvlw80lV+OGUT17sWYRvg3oxshD9A6C2Q8kYL5j0vr5tM5ZIsi6WTxd+2z4snTTpD9LeoFUwoNC3DhiFxmCI3RV3TpRRFqaJjCO5nd3ohy6kcabhDCOnk070jahRMkJFG9AlgBqf78yif26+FuOQyEAqmKFLaKQt15t7NOEyBoFDeYry0xcK0QviS+j4MeE3EnsVW8ZesUqn+LvfV+56pJ47EYr2uF7Q3IZEyePFERF8zUKUZEQSZFD9nMiAnUAw+zdjIonGFE5qlUQ6tKeSNmE49byAWJnOvuULZhUod6zjxu+cWz5OfecGDyhd3D0+MvE/fdiqchHU01KLAIpQCmadJEjFl2fpBA3Ed6M/HofSeCB8LSIgHPQRb5/+wmH2LLzA5/2ZzxvD1w/6/j8uDU34bFhPDbEg7dMssyYko5vGxncWKkPNWTVIXHK7EWQSnowUW/ibroXYiuEzhM3nrFL1CVd07daXnR0KRhnwfljskbD5Epbcvx8fu1G1Irbzx4jGffy+BZHIMx08qTTa8q8MzUqqT4Y8TPElmJxlzBxNDfkhBwjAia6a7G5GNc3jg4JwSO4UCN+heTlXa9wOyfIaC/ChLQ85nRKrCQFOyD4IeIPuYiIo+kc7bVj3DmGnTCeC8O52QZia8RChqzigIrZVWQ0fd9UlUqSOWI2iX4y+C2z/8wlmVSDI8X4qDXrkdPnWYO3bhj8pwq8w29HfsX26wR1nPkj32helpDaT/szXvVnvD5ueX3YcHm9YTy0Fv+eFy4ZDRVBswipMlV9ysie4GQrZtzOVD0npAxWe74RiJ1PmymdH2B8rsRNhM5EkDiK5RSI2ROGFNjUNI4xlcTOLrzsMqu5eraga/aVw6zgis1VJvFfsQYYY+LqYFJ7crf5XvB7KS4qTWP5o9BeGnJPQSla1sANxv1cCsdtriPt9VgK+kvUom5lERk17m4hvIs558WfGgKk+VTHfTovRxZV4IZQxqkJBaRgvaC4PuJ7hz84moNjOFqMQdgKw7Nsl0gRVVkdTO+3vU72i2SXcNUa1MfKc4sZKJ1nYjxgto3MGKgkgPtI+t/JREC7hs1m4Mv+ArD+f+aj3zKo55k/8uXuktfbHZ9sz/h0e87n+y3HoSlIFIMjjA49ekKyQGfxkFi1e6xfSFYZlhNylHJTOTtNFJpr45bAlJCyBTaGiICJ3IpZzBPiBXEM0hTDnEYx33hOya3UD+PGqVCGnva6L54QrewemTslQpg5u/nWob3QYrCKXpBoVvfuMtLsTV8KnTOVKuu8AXwfzVqfjHLSR1w/muSmCkHRjQcRJMSbOdkygUC16GTZ7TerkVK5AssQUU/HyesBuDEmTm9JjBKy3cERU6mxsRfcRqjtRlnimZCdsi/caJKPC1rWIxMEdYKkuAR/NJsSCq41r0s242Qb9Z2GwTdQFZ4UEYjbhm134Fwsjv9lqub62m3JXYkjjouw46PNC761fcZnZ2dcDhvLh0+W9MPYcH3szLVzsCAUOZpvt7h0kl95SZmLZwHzEmib3FZD2hD9pB8CDCn3PO4ibCLiLfSZIKVQho5mWZcxW9TTxhtzMEtl7CyLQeI4FASvm2KgTEa2MZ0/ZqJlhjg/TOKrROPi9pCJU6np0r6PBQndkDZ2hYASFJ8s87FzOcbYRPMQjTsHZ1w5IbUKJTCnrOtSKoBThHZuOn8N3EQlVs8ZI6K5jR04HKIRImYz8A43wLiTkkFYio8wEc4SaZglomTELBLiEAuRd7gU1KQlSGlJmGcQ7xAHvmMlARHUCa2PbMVSh547K+Z4JsdZ9Z5D0/JBc8lX22d8vt1xEbbsQ2sBPaHhKnS87re8PttynTrT7K86wkWLXPiprvyCStfcFZi8DaLE1k6ITXYxWWTaeB4JzwOyCzRNqlo3YkFIPprIHoXuU5/E8WlzTTq3FvdV4U5hTgREp3OmTaplg0o0P3rozG1mcRSarNTp/DEWsV6dTLH00a6P3kE0hHe95XOoT0iZ9Hq/NwIdO1+Oa+1nrCFWOvAaAXgHIJpKfCV1RFIvSzdEew9X4IIzlTGVGHc55oQs7VXuySQBZLtHXsciuaipLG5Q/DCVJC9VhWDGaN5FlbqnQwQAdLJ8e4GOaJ1fxRJ5pnLgB567Ay/9FYfYpTDPqVX1QVuuw4bLsLGutbHj69fv8TOfv+TVp88Yr32JOHS9ub1Kn4imqNhAQpLeECbsQJ8FUz4Vq4qzsfDjJrkNh8EbMmXL+9HhXzdsvym0VzonAomL5zBYu2H6bdSqOk6FTLGukqvT34zkvUObxMkjxNbmIkEXmzHZALLrNNqz45JdoqgDxvHcENDG4fYDcdMQtw3uGJIOLyb75jqRFTzUyPW2iIUoEKK5ElURtRgHc+uBRFfCi3N1IVunWsWabCNSpK5MXBa2itEkKhWHuhQbkSsoFbVDii3mbcLTIQKquDEyjJ4rbWg10jOlE3u1N+NRPEpH4Fx6Whc450huUhpx9CmbZFBbnoDw6fkzfur5V/jx977Kz10+5+J6y3HfEq7MD1zU8aIm6GQtPiZ/cxdpXx7YbYfi889pvjnZx/tI75Qwesajx79u2H0k7D6JNAc94ezFrbbQf82XrgWZ57oy68jixGrk53OYEwjzm1sSkcXTR1QkJeCk31LijQpFBXBDQA4jbBsbZ5Os4SEiMaJNMxn4To3h87l+OySCaDcvFqD07DRJjMdsORLFUoyzrp4lpiS5zL0AmfNPa1rWWicC7J1FKpao1RoSIXjb8HSIAECM9KPnVdyylYHruJl6AQh0SiEE1qUm1wAIs0YhuW983Ufg3B156a/5/u3HfOO9l/z09Zf46dcf8PHrc47bLpWtdjBO98t2A/XKeK64ZwPPz60Lrd1L2A8NY4oN2LQjO1H2LnK138Cxo3slnH2kdBdx0j0rRChEYGklVyYCkKAEzeRrl2K2iHF8EgeXpJNHu496zA2nFNedboThmbeYd5/sHoPSJkkFZ5tdGovNMILhcCGWe6gXi8KsLPqrKoDOn2cV3B2/3wcWY1iciCbDcHJd9hNSqyPZObJqNJdgiujPRADm46fYgZDtBisEIN3LLnjsA87haRCB5LCXMTL0HZ+GZ7QyFk7uslrgYIt1pfVVtB/AuQyG9DJvKBqQVPJLed4e+N72E35o87P87O59/v7ZL+LvnX0XH10/5+c+fo941eAPHhmtCYU2SthZoIn78qEQgJyHX/LvoyNWnYP6saG/7GheeTafwfazQHMd5m4zEYr1qi5/neLf7QGYzgUsWy0FoMS0ZtkHr0n0VwoHs446aeOrEls/M/qFjefwQcP+S46wNTHZDVN3Hj3YvWLj0F2LqDKcbYmt0Fyb/qJpTPI/KuJ0YiPgRIxegj6WU8ZU28lXa5eJZ5yMniYRpDRqL0Ykijown6NUhHR2XNXeRzrfjUkKqA2CSlEHpod83CMu4WkQgQQyBMbe87PD+2xlKPX6vBgROFPj5lklyJA/t4n8brGGJZGYiICUyrytRNpkU2hlLFV/PunOOR69RX7J3NqvKjw7O3K+6Us23hB8Ki9mOnQ/2DhjcFx/ekb3UcP2W6YGtJcj/now8TlksXmB7ADejKPZGDctjBEbIRn0Cvc1TM+ERRtXNrkko2D0YhHTIY0tyUr+vOH4nufqux3DC7tNY5m0ybiViGwwNU2GwOGrZ1x9V4Mb4fznQMamiMRSBf/URtYC9+TwdxGJOyHaf4JLeSFqcQeRlCuSDZ1J0hnTJYWA6TqSZm9HHd8QSAQ2PbPTokqd5Ay8Q3gaRCC/+BDRwfHZeG4VfVJbMY+ycUYUOrHCFLnp6KANA9Dj8RppJdCtyJytRAZ1DLl4J0KX6vKP6mnbQDwbGJOFv92MbLcDPmUPZhhzMhPQ+EhInGsYvAUEXTZ032rYfVPYfKY0R/Nba+tRfxoXP9VXZCICVax8RmYVQZtMJMzFVwpnZJ+1TN/nQTgRyVJCNJF+3DkO7xsBiI3SXAndxSLs9xBw14PNwQsXv7jh+hcJ7QVsP3X4Jhkhh1gSsYApgCdLOt9OKNlPmrw+FTbW0pZQAnhK0RGY7AMVzIyHeew6XiFJciX6VKZzBUp+S3WXOTxS+HkaRABMt2ztcT4bpnJljTPEfu4PbGXg4Fpz6yQJIRODPrbW9APHUO08q+nnuGZyOb6Ku2I8bCWUctvvf7Bn1wzsx9Zy+MU20hgd+74t1XyjCtt25L3tgeuhtUqz1w3udcPuE8fmlSXNxAauv+ThQ9O5c577rK1hqntXLNJQIvVcr1PGWiVimg6bro+WkSfBMvDcqPY9u+dicmcOrpThkhT8kpG0+zzZLS5jSodNocDXZvu4/MHnvP4+z+tfMSK7gPvxTfKT244XTXYD5STCr0B6Nl3GBbxlkCqOQL2Ac0n6MS+JO5qXI3ZuykNIdpniNs1zreNI8jtg+g7ZBbsgNFpdG5nqjzpOBYT60opwPASeDhFQRQ5H3MVLfuLyK9bk0QW23ox/Oz/webfj8/aMM3eclfJuJZiaINFamBVXov0e1CSIPngGzJV47o48lz3vNde86A7wnFJi7HLYcDV21vc+ddPZdQPbxtKDcw3866Hls8sz+s83dB81tJfC5pXSXZiBSL0wbiyBpX8J47mm7jaWxoqjZCpKyrEvuQujlCAgsMCiZp901nGKbvM9NIdYAoUkJjfWOBmxXDhFTN9HNq8sgq65ht0nY1IfkgfUw9UPPOP1L/Fc/NJA95ULXm4GPv/8jObartfWEVuHO4Z34v9+J5ANvmuYVhPZ+0BFGJbj1clra78v7/kYaeDpEAFAjgPNpfC1z99DREt3ISfKxo9WbmzclZJfucCnJ3KWSnznf05iii0wiSFHG/baWNnv5rXZGVzPB901Y3Ts/EBXVR32EhmiZ5OSfrbNkEp0efZDw8XVlvHjHduPPWffUNqrSHcRaa4CohC2DokeUeHsG1paXKk37lRcgwraTB1xp/JXlO44oqRsvJSLn9NwS5ZenCQJNQu4jHGmfmjnCO3kIty8irTXguuV9nJk3DXIRgitMG49119xXP6SCC8GhmND/80zNt/yPPt6wO9HwraxUGJlSgi6SRL4NoOuxCwAJnFmL8ZdiJc5c5Fi0vesptUqUO0NqWX/6tByZUpsSjr3TenAm3Yg+reB3wl8K532B1T1r6XffgT4HVh5sf+Nqv6Hbzi3+0NO54sR3wvXhw7nlDF4hjbV63eRq6HjVbtj6wcr1pGWMNfcA1MfOjfyrOk590ee+SNbN5y0Mj9om9yQHTvXc9b0qfvuPJuvESsUAqYGHEPD1bHj4nJH/HjD7ucc20+U7SulvQy0FyNuCGZRF2gVmmuhvRqnghgkV1Qwg5sEJZy1jM9aalDJxMHm4/dxHqySPQtRk5/aRF1zDdrYFgTk0NY6Hog3PR5NOv9gyJLn648mGg9ngu/h7OsO94+3tJfK2bciEkfay2D2h9FSokWVmCs7PXYrvIU4gho5S/hysr/M7AE1ZmqyZdSFT2fVVSqVLHkaxC0dIDZ29LJaUETS3/reJZO1nssDqcGbdiAC+OOq+kdmjyDyK4HfDPwQ8IuA/1hEfrmqBt4lzFJDKclA1vXGsu9Uhc+hFPLommDVa1NxjLr8lhPlrB04b3pedHs2zqSGnR9KZ5+vHd+nkVC6/LxojgxqnXQPoeHVccdhtGSfjQ+83Ox5ddxZsYmLLfpZx+4jx+5bSnepNPtIsw+442ibZWMWfDcqrg9TqKnON3rsqjZk10ZsZiKkF0KbAqZS8I6EaEE7OZIwGAEtXoKsF4tAI6YDpzh/m4NtYhfUKgc15l5UL3SvB9wxMJ617D62cFt3TH0bPtszvtwRtt6KfySXm3qHPwYzgL5jnf8uKPd3rEsCjqLHZ/fpTe67XDYsdzAuxDcRBF0mM9W2BJjyB2BCcsW8FfV9pEKBN1i+N+pAdAv8RuDPp9Lj/0hEfhL4tcB/8fCpvQFEE2lj8sEHUesrEAVNxTRVzRVz8NbDzrlUwiun5iaJYN+3XDQbPt6f03or7GnlxEMpYAqUY05iaaV9DA0Xx45+tN4B2g18Llu+dXHO9asd7nXD9lOTALpLnZJLUjXb6JPY3YjJUwJxY9VOXIho1tFrN2ECFXNDlZiCoDSjFs+Ane8mD0JMOzIIxJAkgymEV/3kJ89Ve1wS4WVUBGvnpt7Yo78akMNIk5oR+M+uIQR0u0Fbj4SIP4CEaDaELj3XcUzx+G9OBGQttuDBgyzu7ybCZKpVcpMup5lfyUnqM0k9q6I3y3yZzTenrls4eKrJ4HIRVK2uocyHaitonsc7kARugt8jIr8V6ynwe1X1M+B7sLZkGb6Wjp3AW20+sgCNGDJVbbPy8VKgA1fiBQeZimbmij8xePqxKe8oi/TeaSnYAebm8y5aNeNqZ/R9w3BsQGDftGh0hM82bD5xNFdmANx9Emmuo1WzGSPaCME3iTtKsRKPW59EcHCjS7o6RSqQMVXFyQjUSLEXZN1TVEtSj70AUOdSswuMs3tjKSUWIREAVJEhEnet+f2j7eQsFUiqWeCOibOftWhSw+LZxtQKL4Sz1pB9COV3N0aid8RN8wvDHnCTUa9qdTepB8z09okQTJdNORRaCEEhViuShhsizcExHhTfJfWgZVJB6vswP1Y8jg985DclAn8C+NF0vx8F/ijWjuwWm+ni4NtsPpLBV/3ost87BcpopESTWVFQpiYk6e7OW+nw3Kkn99+Ls8IdpLZcdtFxwKSOXAY7wXho4aJBRrEWfRG2nzg2n1mVme4isvl0RKLFGeSIPUvI0eKOi00qeRWzMQm0nXxFEkydyVZ92yyJy8sk3lok/OkyT0lFSjhvjbunwhvZLVaMhinfPzuu1ckUX59E3th5m1MfbI2fdYWAqcvEzjE+a614x/VgdobOpWIcvzDdBJpsAmvIWMOSuwsyFRBZg1IHQYqb1x/Ma+OG5LHJKog7rTlY6lh8u70DqvpRmYTInwL+avr6NeB7q1N/MfD1N57dQyGkttVOiYMj9oLrQlUjLyG8U+sspEKuKixtJIxivQlW4NibM16cEkgEpTL36t6WUlJlnpxt1lwL7aXQXmgKoIH2Os5y8/0xpvRTLeJcbB1h46wVdiPTxqspf3bfVTvD91Nl3aU+mTfyLDGnsjFkm0KxFcRAzu1XJ1NVnnL/erNTSmSRxfw0lqTSYU4xwyPQXKR+kCkASo7BiFGxOay8hOWrqZOcykTuwIYTcT1b+9O1Qc0QmgyCpVhp6yzNOo1fZwnOhqvWJL8zG4sUtZm8Ao2bEQDNEleJzzAJqwRfCQRn9peSMboQ/de57e3LAW/egei7VfUb6etvAn4sff4rwJ8VkT+GGQZ/GfBfvsk93hQkMhW+HJ2tc0Z2sJedF08xa66A3oD8UJ2XegzMjokNJqkeoQyp0KbkQqYy9byPlZEozSHXkLOGlwvds7YuZ990/dI9ZvnXqUBITGnAUc2omK+XLH4maaJs3moOyOmmToft3neJ61IvTh6+nrAuf34r1vwHQ1GJ5FQsr7ZBMRJmQnEPo+VsbU9+PD2UDYTiKqnNV/fWfA6WQbgiVUhSG2erX59zxxK/aQeiXy8iP5yG/2ngdwGo6t8Vkb8I/D2sNMa/+c49A9NE0WhBLzpayS0ZBEZfOLNVi1289PxxkOn7itFHRpmCc8Cy3hactiB9yPp0+jlx7boWQH3/ietMzwK52IepH1r5pQuHSdyr6J2ZsDixkudZF11yyocgXkKSe7nvKiSfNrEudmeaxsyjk9f0/tN6Y1imJWejJ9W61ITByWzt3xROnq3KlBQsAaksU51inJmHmHqgWu8TLRJeLtwKlbqST7tjbm+1A1E6/w8Bf+iucd8VSKCU4pIhi+VJJ204KQc+JauskPBa9M5EoOohXzikWCCPpHJfUkuzSvLHV9/TfQvyu8lnnk8SJ+YZiHG6z2Le6ieLf0a64oKKTFwjSwK1+LpQBVa5/JJY3EE8JGfVCVX8f0UslzBLsb116PvDm0oWy5oLUAhqrpewHF9qwrEChqNVEZeQ38Etc5RM3JPUGCUVNkmvLFJapFncwop0lT/cUxp4OhGD4iAEQ8Leus/KmDmqEQcdJgKwXpxhEsfLienASUmxmqGUGPJEBKoX1exNT5/OrW5XuZtKQ+F877GyJOcCFFniyDpzMhjd5FsvBCFvvjjfMNlFuBqauuSYcHcyj8ylhhMVYlH999vC+ddgphouJcP55+x1uTFs9173qzwCy2eu6ilq9Q8o9gBgIuaVWmjpzlkMXHk+7rfGT4cIJHCD4q9dSZzBYQRhyHoz6zpVQXwqsWr+5pfid7YtZOJQknjUCA+QsseqF1VR+mJxZk4clvp6TjEt885GqbHiuMzHewjcqMe+iaH+BteXjbci9j/EqHefe98lCcTF55oQRKbGtDDZZrKrdDH3wuXvNbessq0Q3gr5i5s3q5ApTiR/V5cym3PAUOqkXJcdm6S9xd8b4MkQgZyP7kZo9qnLDZQecr7Hqvz2WkTpEshRx3FXxEBnBIHSjmp+YzsW2/SbnxOa/FsuRpnbS1EjbNoIqyK5GBGZlQlLFWm1slDfSQBETqQYCzfNOjF3G/7uSoxZhtuW67KktCJdLD//PMEylj/DCeG/B9TnFltNhjUC4N2kdlR1CSSIlaBPxuvJ9gSS8kgi6TjTTUtKcnm22+f7ZIhAbiNkbZxMDK83vRugOagRB4HQWXlnl3sKVCJVEbdqIuClnFZDbZSJiYvkRpPlszLLKdeM2LUVvRSbYM718/3LJqGoEZOPv54Qkxpxukh2SqzOrZinrBCBepzJdPUAWOsU9AuAANyE9DPDaQoMOskTeMP7rRkeNbtgMwGo7FCz95P5RZIO8jF1mO0ozxcq43U1gVv0madDBDQi2y3ttfW7jx2pt3wmDJqaaExkOBMJl3rDxW5C1iwaKpO64KgQKEPizBLAJ3Etl/uuy4+ryBRv7yQVk8x6uhA6N1mLV17Y7FB62dFhlXqLrSAhcsB80mnTaZICcs5+ua9KqnRjG19cJktJKlnEqL+RXrziN59LLNXnWmI4MdJmaljbGNLzJftOGSnZTGYE7CGqRo4KzHMuhtcKG8vapksy3Ri1MOZZpmcdz7FKfJgIjVqpdonG0DRtQFFNwWOUjNKaOGS11FV1Lu/zzp4MEdBxRI89zcECcjI3trbUFAJQ98ADZrp9GWspCSTkMu690KGTDUBkIiCZiLjKOVpKdIugPhftXOeua6LniZuqSC31xC18mHY+ZqlClOeRvBG5TsCaBPBPLdwgWcwqMsH9iEIVp7EWbTmNvXKvrPElAjB1SEr1CLNB14kR8mqsQjiSZ6WW+DKyl3DlfEymvTzzfFXS7E3wZIgAAONooagpndWIAHMJIIEZaKaUTV0WilgsXln8TCCYRH2wxa4NMkWaKPeb7AohvVw3WC5+/eJWA3pgVo5qnjgyqRF53rmE2IxQCEXqyFyfXLR0zWr9TzNkKWAlOvAmmMc1pHJsy2D8lTVa6vvleEb+jMAhVnvAlfdUl4TL11mp+KSWaGoJVxPxZCPIPQ4ysms6Vtu1VuNeFvC0iIBOiTX1whTdaXm6Y9Y3r5xXPk8bo+BaIhZZ5JvpbnE6NhmGtIjj+JRuLiA6Ffs0KYPqTS8IQVR8WEHUGZHS4kos/ypCUoutkt2PKT35rUkCa/r/LyRQTY1O0ve8JxbpwyUuoH6c25Zo6fVIa1yQvybg3k3egBU7gASdGpoKRMxAWKRMwepWpKiMWckxmfbzfZA/w9MhAvlF5g1dLUL04EhVdJP4nolCkQTqlywUI1xWHwrByLECOVf8hulkQpG5cPSSpIWkVgQtc5DUhUeZNssysk9yi+6bHj+lDs9i3vNvIc4RdG2cX+gI/Fi4ITR4KR0Ut2DjpoSum4asxHS7eHoPlgQ2f2fl3TQV918ZvhBmST0Rs+paxQ2sRGiT1dxioL6B+S3h6RABVTTEopOrg9K4M7eEHilWeGuomc7NyF2JzxnpTSqQ2cbJUsN04I7P6WVYBJkUteCm3PSZsSlvsuWGWYq6Oa03SyZUon6daFOJu4XzvSUC8DYKgjw6l0DEcjyiiYJLW8CNkN9vctnFpuouVKlaN0oEanOfzT+pfTVhroOB8nXLcYpkmS62fUBxN5rnyaSBOsZlVozkrvlW8HSIAEAIxQBnZa+rzrpVxl3m8oVKRmwlaqTNyOkqPasW/2eW/3QMTny0dsJCd0xEITZY+S9xJzaLJVjlWyo7wyQpWF6Emzxbswg/mQyF2QYAJxzwKUA23BYDjerUjKQW/RdrMEsUymJ646aeCLV0uYDJGJfUtRS7kcfVJhGS7ArMxV20ek/Vq591kMq/1UwnE4IA4lLzkqyiZjU4S6/rUz6Bp0UEvJ+eOiF9rqmfF06diUuxta7ARf+vpIACN+hWs0SgouOnd5lfUk0MZHE+Wey0eYhbGC5vo+A14apsB3VEW2FGqSApqWzXFLsOszDi1IXoKagEs1ZrSSoonzPc9Dl/r9S/eWLOek2G+QQq9aBC/mVcQI3gdcyC1NffABJBctxJbcNae333oARPiwiEgAwR1zNzp+QW3dlXDhNSapP1/MwNJmrqjhRkLNw+f3fTGG5M7amYdLY8VnEvVjXp3WAvetzaPS2wScq4kwei4uBejOrnOoNOOYkRX0n4KSoF07OUnzPCLJH/DQ2Fq375mzbgUmqp7m1IUovMFWVbyWO4PSFH5tevEQA3fR7Pm3nGpsNE8qqi0sktKiPrDOlWpC1Z2BCAmW0gF2D1R2HcCSGXgQ8QWttDsaiTFIJlY9s+yMztvhGOT4sIxEiusONCFRewpIaZamaxv87dZzr3hIguOXQ5b+WC215AJeJFb+68yf6wuF6Suy/czSFuvNc9GfwJUcheC+6/oe51/5vGWhjRlm5TAEoR1IkorPYfXEoEZdyJ4y7PV2+VmbOLddLdpxiPGXKV8UlEulId8u+LIKcZk5hJLJOkkYm9VJmqZb82afsIp2ott2+7m+BpEQEoBkA/KP4Yi9ssdvVGYBL5MgGoFtpOYkL6GXXndKVv4XbLLsDLrrXZNhBbKTkHc44KJTMv6Fzcnd1Lb94By/1eiZ9z8bkiaJAjse28B6oLs7DcG4jjyXMseg8sCUE2sJEs5+W3hVRkx+bPMt1UCuHI7dHUu9IYNbdnKxLhydpBXf5tVjos1YVchkVnqcYkTJ2/q7wXFVM1vJRxipE7EaToJ6aRr126A4tN4AHwpIiAppdjtgArwOlGJWwm5/6cAEBuH740CkJ6AenYzAiYFnqp292oyyvJJTiVi5qSfsxTEVrBOYqf+FR0n89j2Y0YJqS6kdvdtG5LO0ia82yc+xCAKiouX3cScLOY69p194Gi/ixChE+8ALXLtYZkVFPviJ0nbrxl8NVLkStB5RDwYluaioaWBqNr96ifL787ZV5HMeeBVEVlihcgB3fl/eqnf2tw785HC3jT5iN/AfgV6ZSXwCtV/eFUmvzvAz+efvsbqvq732xqbwArumzR5RfcYxZEtKCk5dqs9wPF1bP2G9M5N6kDpWllfsGBpItOol1EcZo2RbE0whpDO33QaQ0KB38D5HorUEkNa4k6qwRg8Xkt3ViQuYejJgA36fwyIVeWIgSZxP/GEbaeuMk1BG95rjqIa/Y8aTprHCBLdvX7WeyliTnVUYpZD2ByWdd7VVeuhylU+AHwRs1HVPV/We4v8keBz6vzf0pVf/hh03i7MOn5lWgboJjwdU4M7kNBp+SXfABmbhitjudTq5dfhx0vN5HZBiygSdUaeqzq4DelGxdj2eJvfuTlNUuiUH+9SVy/h7FwtrkXhGAV1pB+IUpP5zJZ+vP32c3TjStXoFVdqoJyREpSGGDu2dRcNHTOVLKZTg9Eq1hthUKT+uSsbb1I+q0mTDVkArB8zqQinFjza4m0XMBc79fq2TMjq//ycIngUc1HRESAfxX4Fx5223cMktk/E/eParUGfS3a501a6VZwQmVvKq5RogIBt8LxyhCZYKQsPR91bszJv2nuyEMqLJmJ1mI+q/e42XB4wnlmP9YPdDPi39cweBMhOIGbCMAt1v8TA2CO+swPUXP+ukpPZXibeYG8BQTFZHWvdfWZ/x8KIRFVm2KuFpwj+MaVlO6FPWhZ9CTXFrxNUltWsyoxAFmVzDkilXTw0GzPx9oE/nvAR6r6E9WxHxCR/wp4DfxbqvqfPfIeD4ccuZXUAFMJpIjizF5wLePPhznZ+AsR7iGm2GJ/SMzcVchdaE9F9XPpqJlLaTmdvLFuvfEkSt+1OdbqDa6J5TcPsCCq94CT8RcpwPV97TVWHL8O8qlE/pydV2IkKqkwppZskzqYkLsOzS17h1NxHk4+S7ZBhVhKqp88Y0Xccv8InCLOgrxK7snCSzUz/OX9nCIZZzatPJcH7MkMjyUCvwX4c9X3bwDfp6qfiMivAf59EfkhVX29vPCtdiCqXUgLjqj1i856lpAMLFIWXxbvTjOVTcEYs+wsqmP5BdZ+8aLWJX1hsanVC0GYiZ6SvRhtinTsBZ+bIqgZC3PEWYlirsRKdSblZG42IVKaTF3RprIbZC66KraWCd9zZ5UgdzkhOiqpb0HxSlD+TlZ+i62eVf6NK8KPnzh+zsjL9y2BOcnzE3MyVea2uTZARqKEXKX9+lLqEoprELEYj+IdiOCGmDIEk9GwD5wi/PQE5b3URIzp3eZQ8mIfqET82Ii5CCuJNe/Bmar57fIOiEgD/M+BX1MexHoQHtPnvy0iPwX8cqxV2QzeageiepOeRPxNep5R5Jiaa8pUritviHrIhb51ojdXUoUubjs7J7u7cs54flHUYizERqcXnuYsQXCa1Jgl0ojaRkGnm2eOuUDo8uz5uVaKk6qkey3X874EAKrsvEr6yGtfuJWeLFYJefWCiptJJOIWjDcjfs3pF4FJBcmLFEBBZm2qc7C1qlu8Z69PPV6BRLiLd2CcvAQ5GpPSdLQmqIt3tCbBpDkVN+g9YE3iyqHuD4HHSAL/Q+AfqOrXyqREvgx8qqpBRH4Qaz7yDx9xjzcCzdRbpnTc4tqRFEPQK9pAOHkRVJylOl4vbBYVmR+bXXPTRqruUbixMJWRxt5hiCmPPG3QEh5aPZ9FDcoipyD9tmqpnhKMlnLjic66ppM/xOxcW/Th1DgJJ0FJBXmLyC8n2Z0FcSoxfyJszCWNWipbIFaJ14hTaDk5y3R2IrN3W/o4VCrAVDugWr814nmf9ctJZVktXOxPoNi3lyppLV09BN6o+Yiq/mmsBfmfW5z+zwN/UERGzGTyu1X104dN6Q2gVgdOfktiFky+WLVQYjcA2IuPOuX8L62ts88suGohMnUAznTdlOLMKkHIUIeqxsZ049iCJLHBkTdqff9ECGAKjil644IdLPLaT3THtLHtt3VJ4K69VaQdONnwJROSU2lEFgR2drMKead1nYp+LAnATT70zOntnc+RvoT93iT01O7HLNmsIf/Sm5HBzcdZ7WZUrU3ZCyfIb301RSneitWX8raJwA3NR1DV//XKsb8E/KWHTeEdQV7MaqFqY5AEa/ooQZAGSNFjEWaUeDkmrBx3EJH5fTJMavIppLn9/9s7m1hbsqoAf6uqzjn3vh9e94NuaKHFRnGAA5UYJhhiYqLCBB1onBhNGGLURBMamTAhQRNJHJlgNDFGRQYamSkSjTHxD0jzJ0FAGmno0N3QP++9+3dO1XKw/9beteucc/vdx33vvrOSe885Vbt27aq91//aa5Wx5IDLQDR3i7vp3ES5wiZW9UkqhQvoIWZLKo2GSYTORefkxiJVI7ZBQkaM3SgJlD768l3A2GWXD3N8Xe2e5fsfQn5ELxlh32naPwGu7qPdRGXdeGuJQHgX/l25exjkr+j/1a6aJm3hFjMvMQQ4JDXJGUPg/tawHe1RpbR0Cg0OLkrEYBA9Zx2rRctqv3FI7uvyNSuNhj2HIC5FU7PUKHI7LuKVLL+WSvdMskATg4XEx/zLzJ+zVuYhXTvy905Qeki6qzYuF+HQp5DiRkG93zrJf+lvcpebFbMhk56cRIFze5l3mhnokBS3PwGZJEBFfRiJ62b8kOv3oX047xd37nbzEyW4Yi3iG4d2hsAFxG+s4S6cs49VqAOjQKQNKtNaCBbdpvH2D8lcmWG7eJZrwsYORFWu0rXGRz+1h+BiEAGApqV5xVWOHlpw8LDQrMSXeBZmB84GYBdR0/uS5V6mElWX/WelDL3XyWfEXYZARNxgfFGDI5abO85DzD4UXD9RT7WTFBEAT3nSfUKcuAzqCqgEtUOTtTvFsntC1YSdhokrRptBaUDVdH3mQzfjyvYxbDBYjfRuSzjDPQnvIjuRrq9wskDYgi5OsHk0hujZMOoahy84dm78lFz6icf99+A5aMz1VIhcDQLXDxJA0zB0TZJUpZACYmxDReJMwzU/jDSgp9YEgAtEBJr5DL12lcNXNhw95HS97qbQHbmX2R0515v785IBbvE3vSJLx3mHVhh8duJ+josqazFbjiG6ZCzka8ohrzHmxTBh29ZMWggQChmKo2GoAVFhmDniFMqnhd1oqkT3YlgQQd+FUoIZLxHpE8LIqoIAZsVtJAKFJFCz04yMf6MGBhEtIRrsc+X9jsR56y4uw4yNSC8lUfRIug6mOH4Wv5BdIKnv6ML077I1qlEj2T6WmpHPRpJPeau0PLcFXBgiADBcWtDvCdoOaAerqzAsoF8I3aHQHSizW74WQeDkjUO89nCIvuWQd6CfC6sFDHO3lxtxxrp+4RCvWbnre1OvQDyBGDp8xl+HhaEsmSUKoQ/UEZlUFouMEyiw2ndSQT8T2hONKdPCrI8NfUkkHoG1B4SkKwM0xjQeuZ3Fk6DzxgUq2YILPupw3wzhxYxXmdybH0Vsw9Vt8pNqgFThl8+Oj0R3TxyL+wv4lGQVQmhvtY2NInaaSwAprsFLAP69DCbjcNgtGGogxiHbOQuE3aiUWbxLwZA2wcUgAiLoaoX0Pe2x0h4J/SIh6DBXVvvQ7TnE7g6U7jjZAzREYeE4Y+MLiLZHQrvXsFoIsudedL8A1O0GbI99NeJLPksR5OpCG8KD1Vv6MTYDp+cPnSNCIRFEtjYtgjVuu3Ezh95LKrGoqhWhJSFj05t8CuUrU/+swUsiymDqFYgPbcyMllE4GCM/4KLlfChttsWW1Ed0rxmxffS8pSgfXkpNXy9hjZW+moYcEkKG69fBJh+8sfyrSHJlmqCm6Mv3EkFUB0Kuy9YTgorXJKiD2S010Qk18xSGs0kyuBBEQNoWHZT2+Zvsf+cax9daVpdx3MeI18McTq7B6pI4BD/2yUcHj9Qn6oqXHA8uEKRXuoOB9hialZuR9sTVOQzt1dceDNb8YUGsR9ioxEIjQ9i44mO9k0UehiEYJT1ii1LaD9rWEZpmjiuBXm43DsTHX9P0ICtiDcRIHILo2EO7BF0GoiFIl4hGiK4UY0uoVUay4BatJx4kiSDq7TYPfyHCZyqL5fiV4Bt3QfE7BioxRvzymgqiCx5pKwJKtndhCyKQAsMMATBbhUcuzugtISFs+WnBIrqf2ygdaC7MoGyUBi4EEQBg6NEXX2Lv2Ye4dGWP5WWSvj84BF1ehdU+9Aul3xdkKZkFvzvwUsKhE7nbpZMKZKXMbjo/8tAJ3dzp383SBRw1qwZtYLUHq15gL6fq6l2QeFtBmMOokkT3HtBo4hQQG/dzpWkFXRGLUYpdDIYIyADag7SCmuKoMmi0IbhdjeI8ENHwGdyL3h8ddl4GiWMiCjH+Dj73oL/3ufgeIzcru+5yicOfD+J9RRKY1M2ji3BCrN/Gin8bECsYm4CnODeh5kGw+GfSVCIKkxGLBqIxUIgxKLG6VvmI9w0REGfpb45XzG+4HG2gcVGu9hxFDjr90Jn4eY+w/UJYXRKaE8dp22OlO3SFTLuDIXKwZgmyUtoTtyrbI4dIs5nQ32xY7jt1ZJhLrFYcRbyQFMKKhpIiAkUDVyJydbG75yzH0HSNmnUXMi0HF2g41h0FZDTqgk/DlhnkvG0jhcO6c7Isgh6sR9Fa1wdc4gwj1sfhWySvzKElAK7jCte+w4h8KjAhwPHTBv6E34VnpGpbMFLBKFLQLgHvvs2YQFgDhSRUXFqFC0EE1LvEZD5D28bnFgzsz3/4uIDmRNwe8ciOnZowzNQTAuj3/AvthfbY2QZmB46KB+9CQKj2eKA97mM0n7bCfK9leblhtZ+MO8PM6f393ES1CdE1NMwwonjiBtH/a+IVpE9/qapyeBlufO2JU1ec2uB0//ZoiG1S30ZEtzvogrfA/j72FMVburNtuyJZxpyRpR4SItSgPBcWs9XV/bHbqm9Q6v5lX6WHwzSNRMog/MgrYJG34k2ZKjhi40hs6HoWxg5G+Te/8e+7wW9Br/S/Bi4EEWCwlT+TSArExJEuUARmt5x+Hs5rA3pMdLGFaq/DXONOw34BJ9dSarD2RGgPYXbQ0B26GITuoI87y8Bx2WZpklSEHYVoimQEF23Y4gujBIOfpsl2l7j89fF5LRdPxAPcte0S2pOB5ljdLrdiwUCdm0poO+Fnz3VjT3h9vAFiJIWa3uxVnex3gIn2VY6/SSf3105FPdbCdWMhFv97EkqjXwOlJGDdpDEOIAQCCdleB9cPcYfgEErPG9WuCpr+svgUr2qM5nuD4HQxiED0/WqGQFbPcoY8s7ACxy3Cg4OBr19ILP2sXlrQzunrw0JY7cPyqjcwnijdgevEcnFr8QVSrQPxrkHD+dsDpTlxxrqQkjzbu9AUW1KHPKNy2AjjjH5OVWlWQwyuCe7PWhRd7DNw8gmf+2hvgPXHG3Wilv1XVOv6emhfEAgxfdvjayEgpenLjsWpWRK/E34bZK5JGVnb8l4F509JS+x+BhMAFN2BqfMYJmw9AutoUVjimR1l4jub1acLQQSCdwB1ervNHmMNMO1So79+VEHIIO7gpYF+Tsw6M8zwvtskLWjniMNqJSyvEKl44NAqXqqYkVJNi7teVp4orYAGmgOYHSrdoaZCJB6x7HbYCFEt0BQaHQxvYaPRYBDYE4fM9263WEPhnwzH/LBLIhA3G6VrJnMJRu+C5sQjvZKYoSd1NiEFbCIE5XUBqQo1YiTGh7FVJI3MK1Dh/HYvRLbnIej/Yf4a8x0So/Kb1qINAercWyu0QfO2UvzeBi4EEdCwgSTulMNnFJIoJiWEcB9lVFoEL7LZKkU2+KdfiCcORAOfdtCbLLDB/68trC4p/b4j3e2RU0v6Pfd7dqOhO0jifHekzG6swCfDaJautqJ2aXEF/74NBsoi6rIXYwgDjA10pl1mjKu948J9VuPU2QKtcfIKARi1sW3L36qU6cVjAFPDdP+QEYPRMTuO2nkrKRR6fkpQIqPcBjavgWubGI5NbBJToMXrQudBeiGe2wqKdptsKBeCCDD0zqW1XGaVYKTXNM8W+a0eVeKNuBOt19MC5R46H7F3lCII7b6CKB2IR9TB27W8HSG6bgZcQhMzLhkcAWiPBtrl4IxsrcS0VTFDURh/EOXLgByBnCtoHfFrLrMpHTx7N4U4P9V+3fFtjHqb2hiJYKSbbwKL+KVlf6vzhTThJYDo+28KZG4K8d9EpUZJoBIuvAmCh6B+crs+AlwMIhCotDFeRdA64ltxGIi54SKi9NCYOPWgt3U+EWgw4kTrfufVgy7dx2WvFZqlQ+Lu0KkJIcy4O4TZLWcLmB04IiAng5dqzEYglRyBNSF4NOSFIJVN3L6EU1iSN7rmpgJ6bCDPuj5M3P463dy1LSSMbSP+1ln2R2MIxwziW0mg0PtjqHeWIoxMJU1IbwhB2DF4Wq9HTagqu9iiy4tBBCBNviS3XCpRLglxgOhT9W1cwIWS6XPR2OUODYQ4/+QrtxMcq9fEpCTC0CmrW059QKA7cIa8YebUje5YE/L3GvPVJQWxIGBheCUB8M/vDINrxO0JhN/W7z7Klbftog25D7doPzK+QQyw0UHqa9ogtGzhhpy07HvOPRqLFdwKIpBlMQ6cv0IARnkN4/GkImwNFRW2+kmFKFRgm8xCj+JqDrwGNx0fVtU/FJHrwF8DPwA8CfySqj7vr3kv8C6cuec3VPXvNw/lNsAs4lwkCxtePCL1vtZbEM3DMw5Jd3YTb7mp32nokc7q3YGq07gkmiGppbvQnesWTSwz5vz2Gm0Ozcr57ttjn6jSbJSJCzQkBV0n1m8Sz82z2LGPoupOwYkmkdXeq3TNbeg/c9VBam+zJw2SnctsAoDWLHvlOMP1IkZfHyN8RhwCWBdgQGyzDtYRAHtdigfIz20NU1NdIwAb+t9GElgBv62qnxaRq8CnROTjwK8Bn1DVD4rI48DjwHtE5E241GM/Anwf8I8i8sOqWtp/zx4CIgcxy/1zwUHRykLk8NatFUB8VGDJ9Zqlaeepv5D0MhnUuc7tbjUhlh8b5j5noBfLWyH6+4G4jVfbJh9Tk0qKuwZjrl7d376NPz0OdTtJIIOyf+uam/DTbxR3p3z19neQKjwokhOYbTJqBCQtshyNRemxuqAVm4DbHSh5HxMSQHaNCSne1hYQHlHdw2/P9dec2ya92NO4VOKo6g0R+SLwWuCdwE/5Zn8G/DPwHn/8Iz7z8NdE5CvAW4B/23Sv24ZGoGuiWB4Qvlmpj3/3by2I2IESe5BgUxhwri/DYWQ1pBcZNvl0jUtOMuDi5EeLiJjHsD1O+mKw7od7AkadCeM2UoHV5yd20m1DAErDXpQGTmtUs+1r1xpEVSS3CayDTf3CWGLJpJs114VulToB8Pv5a2OtuhahTgCa1CZ6jwy3D8FBschMOG5dh+sgrpu6MBBiYvzwzkYdsOArEf048B/Aqz2BQFWfFpGHfbPXAv9uLnvKH7vjIF3Haq/LQnODXSBE5JUSJ5gMvIpD5mh110QoIoUPiESKkAMvwru0ZVkiiNB+UFf4ws+eIyqJ02Tb2NeJ6k0+9RmS+b7sOLM+TP81OG0OwWyBNZKX3bYQxrwtsam0iypcaQAUi4C16+rvIXPtGd0+JG6tpjkrvsd7GvE/+P0h5/R5GLBVB4w6EfouOPxWYAShMux8k2y0NREQkSu4JKK/paovrTHA1E6MxnGmxUdCn5cucfPRBUfX096AZunCd9sT6FxFhFjdx8XkJySVHqRpo0qQVZytZZyxiBryERhjYra5W5zff3JHm10AGELQCNq0TnoYhqorL7b1ngS3j73ygir6tnUzyWoi8GeCQ2XBVkFiqTaU/HMDVK3y3lgbbSPZ+GQ8ZhgRbkjPG6UGw7ltlGmGtM20qpAZ9crvxTisCpA9VyQG6XO0xdoNPlMfsuQtkJiZuR7ZLBFsRQREZIYjAH+hqn/jD39bRB7xUsAjwDP++FPAo+by1wHfKvs8y+IjsligyxVy5RI3X9dw6/U9zUHD/AVhfkN8Cm4fo68weCSxcfdhA0bY8ouStn5CdAGNorPK57JcuXI+2rfDfcYNxoQgnhNotBqWG3ermXDWcaP1r3kS8Q0Sl9w/u7a5rWl0MGguqpsx2Fe2dhdebFOM30IF+bN+wvVFHoCsb4PwmQoQxmcQdhQMZP5OYw+YBLtMBh29n3W32MY7IMCfAF9U1Q+ZUx8DfhX4oP/8O3P8L0XkQzjD4BuB/9x0n9sCHzKs8xmHDykPP/Ydnr9xiZPmEjI00eLfrEAHiXHXA2kjjtPPJGd6intDSiV/XsFFrU4fjlXEY5f4IYn347RgdqWbxdniowIZGcfSDWWNaCwxpfgkWOmhhkyb3GebOP02NCJslCoJjki63p+rcsuaMW+CYIw4fglTBU1q1wfvRJO3Lb0FIzWgIgVsBMOn8BJBZu8JNiXxa7RgXiVsIwm8FfgV4HMi8oQ/9rs45P+oiLwL+D/gFwFU9Qsi8lHgv3GehXffac+ALk/c56JjuDTwmss3uDw/4et9w+FsQXezYXbDWei7g5BNSGlWzkUkvUabwSAgTSgDZvToQAQ8pxql3gpgOXmbOHnubfBfBjPpPi4h51rm/kgsyRXbFUayzOBVgw2W81HR0gLpN2bZ3Sjub6ACUnDdApFF83urJ4xVhJfid3afcI5sNx8UhKW09pOL81aP14JQjJA/IwJmjKclAPFBSWtNyCTGLMsQeV7IGmzjHfhXprv46YlrPgB8YFPfZwkymzNcmqON0jU933/leS7PTnjmgSt854UrHLwwZ/lCy/wloTvw0XtLUrJPcGXLw179AWgrCTzD/DWS6uxRSAV2sYdFFVOEa7YIkhfAORzzW+UEIYr8QTeOXC8sQLu4wuq0hGyD+XnKeGgkk9EzvBwj1tTtw7hLRAnj0fRuMwmpbEd5rHKzUbqvMIjiGsO1IYn7mQphEDjL0mQQP/S5UfrYAkrmMxImS2llA1yYiEFpGwaPDIMK++2SN1x5jlfv3eDJxXWe3nsFt/b2GeYd8xedijA7ULeLj+DDB2k8MfAx/1HK74MMJonSiqe6gxboan4VW0NHyBO1CAWr62uKYMwkBE8D8Jy5iqyTnHrDS4z59QuuHyUXw23iwjbnT7t9bTS+NQTAirbmnCVC46Ac0/do40+6Z2a9t33LuM/RPBpOnj2HPVeOpUY4M3sH02CMf2ntFE3MuxGnt66FC0MEhqMjmuMVyIJr8yPmzYqToaORgUcuvcis6Xl2vuT5xWWOZi4VsQwu2ShCIgKD37gjktXIa5bEtOKqeDeiRwCvHqTBWEkgcJC8TZpwSSKdCbaxnLxcFOJXwNax5mHRbNt+CkpVo7bwzwq27W6bdlP1EiaNi+5rFuhTu9fLeORIuI1eXwghm/s1yK9lJ5BLr1vsDbkYRCCKyCD7K75//7v02vDN5T69Cg/MDrk+P+D64oCn5g/wTXmA434PGRoXzusLkyQk9dTfIG3MAR8zOkhM3BmvqXx1EYuJw5bJOqtgg1ZK3d/6x9cEzmT3Py0Eu0fZlUmYEY+dBhE2EQorBVCIsmfgeNgKtiUA6yDMy4SqlBvykvSRJZLZdrhG/y+Pu7GMz5VwMYiAX/yy7NFlw0E/Z6kt++2SBuV46HhgdsiPvuIbPLL3Iot2xde7BznY32f+nLMTaCt0h9Aeg4j6KsAUNQxJL9TQC3B7A1KkXzhISjlmNi8B441BVn9Toq4b+ksIIQbZJaf0se+83/I9Tb7GrhkvGCtBVIhRfq8K4Zha1BVjXulDdweLn6eoglTzGoy8Cv4vIL0MGreQB1Zts1UNlRyE1hDoOlZQdRLbkO6BmGVjvUBG5RDI3MyxS/NqUiQidbWinP8NhOViEAEPIVf+4P96/+ZaUQYVXlxd4njouDo/4uFrN3lW4Ej20VlLd9N5D1q/3bdZulRfIY6gXXqznfg9A6F2oIcQpkyWzsqFFIvRDRPFlzHCYdu4hVcT+TN3UCXbzsiWcJq9AZarlCqEMaDV9vNVEX7CR+/aj/Sciurz8qDqNqydG103IQF5RlB6EWJ/1m25jWr0ch/sDsCFIgJBJVhqy+DluF6FVpw0cDx0rLTllYtbPDg/5FX7N/nG/oM8t7iKyjzu6+4OC5+6JErsUoM7BK6l/BqR4YYsRjy2CzCRyz8zghUwihSLtoRcNViHsJNQCWKqctRqJNQ0wuf9WcJiJAGhGo9xGphC/szaX0hxVdHfCl4NsThs4LRaG5vkSyAzChaMII1rep6/V3CxiMCq95JAvvJ6FWwkzH675Ep7zAOzAy51S0SUZ/trDPMuhgc3S7wo6C8KXLAQ4zPOoFBiuzMk5v73jFYUnEXBu//y4xaiYclu0sEQg9BxzDC+HSbVuFgUc1Wz9FmT4xp1WjZihHzxVEjIWba/DciJQmVMYSw22CdDUmJK+KETE2GqWbRpIrjFkC0hKO691ptxG3Dafi4WEfCSwIDQFKun/L3UlkaU6/Nb/NADwrztee6lyxzt7zN7vnViv6ZYgvbEkPeC0jsjIiMCAX5jSMldrTEnII5N+tmkYyPxuIYUFql8/2CJwnarQnszBgwB8IlZMh931eJV6XMDp8uiESttT0MD6hGE68doY/fD+fJ6mz0qIv4gbrjqk7h6JqDFI2QhwrX72nbnBBeOCIR6eY0MzHAEoTfHAFZDy2poXRvpeWTvRS53Jzw5u85TAke6T3vc0R47VSIGEEVuT0WkzBdh4A7RrWj1+NqE24gz/yy1YCWNG2iorxx1tgjXl5wKixIN0fwAnC4/wRqYjki8fUSYRP6S+5Kf05qNwsxrHJuQohRDs6nQcNv/hORTbX8WcMr+RF9OQokzBhF5FrgFPHfeYzkjeBUX41kuynPA7lkAXq+qD5UH7woiACAin1TVnzjvcZwFXJRnuSjPAbtnWQdnJOTtYAc7uFdhRwR2sIP7HO4mIvDh8x7AGcJFeZaL8hywe5ZJuGtsAjvYwQ7OB+4mSWAHO9jBOcC5EwER+TkR+ZKIfMXXL7inQESeFJHPicgTIvJJf+y6iHxcRL7sPx8873HWQET+VESeEZHPm2OTYxeR9/p5+pKI/Oz5jLoOE8/yfhH5pp+bJ0TkHebcXfksIvKoiPyTiHxRRL4gIr/pj9+5eVHVc/vDxfJ+FXgDMAc+A7zpPMf0Mp7hSeBVxbHfBx733x8Hfu+8xzkx9rcBbwY+v2nswJv8/CyAx/y8tef9DBue5f3A71Ta3rXPAjwCvNl/vwr8jx/vHZuX85YE3gJ8RVX/V1VPgI/gipfc6/BOXEEW/OfPn99QpkFV/wX4bnF4auzvxBeVUdWvAaGozF0BE88yBXfts6jq06r6af/9BmCL/dyReTlvIvBa4Bvm9/esUMkZggL/ICKf8rUUoCjMAjw8efXdB1Njv1fn6tdF5LNeXQgi9D3xLOuK/XCG83LeRKAW5XyvuSveqqpvBt4OvFtE3nbeA7pDcC/O1R8BPwj8GK6U3h/443f9s5TFftY1rRw71bOcNxHYqlDJ3Qyq+i3/+QzwtzhR7Nu+IAtFYZZ7AabGfs/Nlap+W1V7VR2APyaJyXf1s6wr9uPPn+m8nDcR+C/gjSLymIjMcdWMP3bOY9oaROSyr9SMiFwGfgb4PKkwC+SFWe4FmBr7x4BfFpGFiDzG96KozG1CQBoPv4CbG7iLn2WLYj9w1vNyF1hD34GzgH4VeN95j+eUY38DzjL7GeALYfzAK4FPAF/2n9fPe6wT4/8rnJi8xHGUd60bO/A+P09fAt5+3uPf4ln+HPgc8FmPLI/c7c8C/CROnP8s8IT/e8ednJddxOAOdnCfw3mrAzvYwQ7OGXZEYAc7uM9hRwR2sIP7HHZEYAc7uM9hRwR2sIP7HHZEYAc7uM9hRwR2sIP7HHZEYAc7uM/h/wEu78CFI3dXvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(calmode.activateinfo[0][\"input\"][0][0,1,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0 # 确定展示的批次\n",
    "j=0 # 确定展示的具体成熟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infodata=calmode.activateinfo[j][\"output\"] # 获取对应的数据\n",
    "if len(infodata.shape)==1: # 分类标签\n",
    "    pass\n",
    "elif len(infodata.shape)==2: # \n",
    "    pass\n",
    "elif len(infodata.shape)==3: # \n",
    "    pass\n",
    "elif len(infodata.shape)==4: # 常规图片\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(calmode.activateinfo[0][\"output\"][0,10,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
